diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
new file mode 100644
index 0000000..4c087f4
--- /dev/null
+++ b/.github/workflows/ci.yaml
@@ -0,0 +1,139 @@
+name: Build Image
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+on:
+  pull_request:
+  workflow_call:
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      max-parallel: 5
+      matrix:
+        chapter:
+         - name: chap1
+           folder: Chapter01
+         - name: chap2
+           folder: Chapter02
+         - name: chap3
+           folder: Chapter03
+         - name: chap4
+           folder: Chapter04
+         - name: chap5
+           folder: Chapter05
+         - name: chap6
+           folder: Chapter06
+         - name: chap7
+           folder: Chapter07
+         - name: chap8
+           folder: Chapter08
+         - name: chap9
+           folder: Chapter09
+         - name: chap10
+           folder: Chapter10
+         - name: chap12
+           folder: Chapter12
+    runs-on: ubuntu-22.04
+    name: Image ${{ matrix.chapter.name }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Extract branch name
+        shell: bash
+        run: echo "branch=${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" >> $GITHUB_OUTPUT
+        id: extract_branch
+
+      - name: (GitHub hosted) Free up disk space
+        shell: bash
+        run: |
+          printf '\nDisk usage before cleanup\n'
+          df --human-readable
+          # Based on https://github.com/actions/runner-images/issues/2840#issuecomment-790492173
+          rm -r /usr/share/dotnet
+          rm -r /opt/hostedtoolcache/
+          printf '\nDisk usage after cleanup\n'
+          df --human-readable
+
+      - name: Build Image
+        id: build
+        run: |
+          cd docker
+          docker build . --target ${{ matrix.chapter.name }} \
+            --build-arg branch=${{ steps.extract_branch.outputs.branch }} \
+            -t graph-machine-learning:latest --no-cache 
+
+      - name: Test Image
+        id: tests
+        env:
+          KAGGLE_USERNAME: ${{ secrets.KAGGLE_USERNAME }}
+          KAGGLE_TOKEN: ${{ secrets.KAGGLE_TOKEN }}
+        run: |
+
+          docker network create my-network
+
+          # Start Neo4j and JanusGraph if we are testing chapter 10
+          if [ "${{ matrix.chapter.name }}" == "chap10" ];
+          then
+            docker run --rm --detach --name janusgraph \
+              --publish=8182:8182 \
+              janusgraph/janusgraph:1.1.0
+            docker network connect my-network janusgraph
+
+            docker run --rm --detach --name neo4j \
+              --publish=7474:7474 --publish=7687:7687 \
+              --user="$(id -u):$(id -g)" \
+              --env NEO4J_AUTH=none \
+              --env NEO4J_PLUGINS='["graph-data-science"]' \
+              neo4j:5.26.0
+            docker network connect my-network neo4j
+          fi
+
+          # Start Neo4j if we are testing chapter 13
+          if [ "${{ matrix.chapter.name }}" == "chap12" ];
+          then
+            docker run --rm --detach --name neo4j \
+              --publish=7474:7474 --publish=7687:7687 \
+              --user="$(id -u):$(id -g)" \
+              --env NEO4J_AUTH=none \
+              --env NEO4J_PLUGINS='["apoc","apoc-extended"]' \
+              --env NEO4J_apoc_export_file_enabled=true \
+              --env NEO4J_apoc_import_file_enabled=true \
+              --env NEO4J_apoc_import_file_use__neo4j_config=true \
+              neo4j:5.26.0
+            docker network connect my-network neo4j
+
+            docker run --rm --detach --name ollama \
+              --publish=11434:11434 \
+              --volume olama:/root/.ollama \
+              ollama/ollama:0.6.4
+            docker network connect my-network ollama
+          fi
+
+
+          mkdir -p data
+          chmod -R 777 data 
+          docker run \
+            --rm --detach -v "$(pwd)/data:/data" \
+            --name graph-machine-learning-box \
+            --env KAGGLE_USERNAME=${KAGGLE_USERNAME} \
+            --env KAGGLE_KEY=${KAGGLE_TOKEN} \
+            --env NEO4J_HOST=neo4j \
+            --env JANUSGRAPH_HOST=janusgraph \
+            --env OLLAMA_HOST=ollama \
+            graph-machine-learning:latest
+          docker network connect my-network graph-machine-learning-box
+
+          # Run tests
+          cd docker
+
+          ./tests.sh ${{ matrix.chapter.folder }}
+
+      - name: tmate session if tests fail
+        if: failure() && github.event_name == 'workflow_dispatch'
+        uses: mxschmitt/action-tmate@v3
+      
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..337d25d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.ipynb_checkpoints
+__pycache__
\ No newline at end of file
diff --git a/Chapter01/01_Introduction_Networkx.ipynb b/Chapter01/01_Introduction_Networkx.ipynb
index f7be5b6..62e72bf 100644
--- a/Chapter01/01_Introduction_Networkx.ipynb
+++ b/Chapter01/01_Introduction_Networkx.ipynb
@@ -4,52 +4,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot Graphs"
+    "## Undirected Graph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def draw_graph(G, pos_nodes, node_names={}, node_size=50, plot_weight=False):\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray', arrowsize=30)\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "    \n",
-    "    \n",
-    "    if plot_weight:\n",
-    "        pos_attrs = {}\n",
-    "        for node, coords in pos_nodes.items():\n",
-    "            pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "        nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "        edge_labels=dict([((a,b,),d[\"weight\"]) for a,b,d in G.edges(data=True)])\n",
-    "        nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels)\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Undirected Graph"
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import draw_graph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -61,14 +54,43 @@
     "E = [('Milan','Dublin'), ('Milan','Paris'), ('Paris','Dublin'), ('Milan','Rome')]\n",
     "G.add_nodes_from(V)\n",
     "G.add_edges_from(E)\n",
-    "draw_graph(G, pos_nodes=nx.shell_layout(G), node_size=500)"
+    "draw_graph(G, layout=nx.shell_layout, node_size=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Rome': {}, 'Milan': {}, 'Dublin': {}, 'Paris': {}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(G.nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V = ['Rome', 'Milan', 'Dublin', 'Paris']\n",
+      "E = [('Rome', 'Milan'), ('Milan', 'Dublin'), ('Milan', 'Paris'), ('Dublin', 'Paris')]\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"V = {G.nodes}\")\n",
     "print(f\"E = {G.edges}\")"
@@ -76,20 +98,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'Rome', 3: 'Milan', 2: 'Paris'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "{G.degree(v): v for v in G.nodes}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Graph Order: 4\n",
+      "Graph Size: 4\n",
+      "Degree for nodes: {'Rome': 1, 'Milan': 3, 'Dublin': 2, 'Paris': 2}\n",
+      "Neighbors for nodes: {'Rome': ['Milan'], 'Milan': ['Dublin', 'Paris', 'Rome'], 'Dublin': ['Milan', 'Paris'], 'Paris': ['Milan', 'Dublin']}\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Graph Order: {G.number_of_nodes()}\")\n",
     "print(f\"Graph Size: {G.number_of_edges()}\")\n",
@@ -99,9 +143,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nodes: ['Rome', 'Milan', 'Dublin', 'Paris']\n",
+      "Edges: [('Rome', 'Milan'), ('Milan', 'Dublin'), ('Milan', 'Paris'), ('Dublin', 'Paris')]\n"
+     ]
+    }
+   ],
    "source": [
     "ego_graph_milan = nx.ego_graph(G, \"Milan\")\n",
     "print(f\"Nodes: {ego_graph_milan.nodes}\")\n",
@@ -110,9 +163,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V = ['Rome', 'Milan', 'Dublin', 'Paris', 'Madrid', 'London']\n",
+      "E = [('Rome', 'Milan'), ('Rome', 'London'), ('Milan', 'Dublin'), ('Milan', 'Paris'), ('Dublin', 'Paris'), ('Paris', 'Madrid')]\n"
+     ]
+    }
+   ],
    "source": [
     "new_nodes = {'London', 'Madrid'}\n",
     "new_edges = [('London','Rome'), ('Madrid','Paris')]\n",
@@ -124,9 +186,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V = ['Rome', 'Milan', 'Dublin', 'Paris']\n",
+      "E = [('Rome', 'Milan'), ('Milan', 'Dublin'), ('Milan', 'Paris'), ('Dublin', 'Paris')]\n"
+     ]
+    }
+   ],
    "source": [
     "node_remove = {'London', 'Madrid'}\n",
     "G.remove_nodes_from(node_remove)\n",
@@ -136,9 +207,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "V = ['Rome', 'Milan', 'Dublin', 'Paris']\n",
+      "E = [('Rome', 'Milan'), ('Dublin', 'Paris')]\n"
+     ]
+    }
+   ],
    "source": [
     "node_edges = [('Milan','Dublin'), ('Milan','Paris')]\n",
     "G.remove_edges_from(node_edges)\n",
@@ -148,18 +228,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('Rome', 'Milan', {}), ('Dublin', 'Paris', {})]\n"
+     ]
+    }
+   ],
    "source": [
     "print(nx.to_edgelist(G))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Rome  Milan  Dublin  Paris\n",
+      "Rome     0.0    1.0     0.0    0.0\n",
+      "Milan    1.0    0.0     0.0    0.0\n",
+      "Dublin   0.0    0.0     0.0    1.0\n",
+      "Paris    0.0    0.0     1.0    0.0\n"
+     ]
+    }
+   ],
    "source": [
     "print(nx.to_pandas_adjacency(G))"
    ]
@@ -173,9 +273,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  source  target\n",
+      "0  Milan  Dublin\n",
+      "1  Milan    Rome\n",
+      "2  Paris   Milan\n",
+      "3  Paris  Dublin\n",
+      "        Rome  Milan  Dublin  Paris\n",
+      "Rome     0.0    0.0     0.0    0.0\n",
+      "Milan    1.0    0.0     1.0    0.0\n",
+      "Dublin   0.0    0.0     0.0    0.0\n",
+      "Paris    0.0    1.0     1.0    0.0\n"
+     ]
+    }
+   ],
    "source": [
     "import networkx as nx\n",
     "G = nx.DiGraph()\n",
@@ -189,9 +306,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Indegree for nodes: {'Rome': 1, 'Milan': 1, 'Dublin': 2, 'Paris': 0}\n",
+      "Outegree for nodes: {'Rome': 0, 'Milan': 2, 'Dublin': 0, 'Paris': 2}\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Indegree for nodes: { {v: G.in_degree(v) for v in G.nodes} }\")\n",
     "print(f\"Outegree for nodes: { {v: G.out_degree(v) for v in G.nodes} }\")"
@@ -199,11 +325,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "draw_graph(G, pos_nodes=nx.shell_layout(G), node_size=500)"
+    "draw_graph(G, layout=nx.shell_layout, node_size=500)"
    ]
   },
   {
@@ -215,9 +352,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  source  target  weight\n",
+      "0  Milan    Rome       5\n",
+      "1  Milan  Dublin      19\n",
+      "2  Paris  Dublin      11\n",
+      "3  Paris   Milan       8\n",
+      "        Rome  Milan  Dublin  Paris\n",
+      "Rome     0.0    0.0     0.0    0.0\n",
+      "Milan    5.0    0.0    19.0    0.0\n",
+      "Dublin   0.0    0.0     0.0    0.0\n",
+      "Paris    0.0    8.0    11.0    0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import networkx as nx\n",
     "G = nx.MultiDiGraph()\n",
@@ -226,7 +390,7 @@
     "     ('Milan','Rome', 5),('Milan','Dublin', 19)]\n",
     "G.add_nodes_from(V)\n",
     "G.add_weighted_edges_from(E)\n",
-    "draw_graph(G, pos_nodes=nx.shell_layout(G), node_size=500, plot_weight=True)\n",
+    "draw_graph(G, layout=nx.shell_layout, node_size=500, plot_weight=True)\n",
     "print(nx.to_pandas_edgelist(G))\n",
     "print(nx.to_pandas_adjacency(G))"
    ]
@@ -240,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,9 +425,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from networkx.drawing.layout import bipartite_layout\n",
     "pos = bipartite_layout(B, bottom_nodes)\n",
@@ -279,9 +454,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import networkx as nx\n",
     "directed_multi_graph = nx.MultiDiGraph()\n",
@@ -290,15 +476,22 @@
     "directed_multi_graph.add_nodes_from(V)\n",
     "directed_multi_graph.add_edges_from(E)\n",
     "\n",
-    "draw_graph(G, pos_nodes=nx.shell_layout(G), node_size=500)"
+    "draw_graph(G, layout=nx.shell_layout, node_size=500)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap1",
    "language": "python",
-   "name": "python3"
+   "name": "chap1"
   },
   "language_info": {
    "codemirror_mode": {
@@ -310,7 +503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
diff --git a/Chapter01/02_Graph_metrics.ipynb b/Chapter01/02_Graph_metrics.ipynb
index da7aee7..9be3d4b 100644
--- a/Chapter01/02_Graph_metrics.ipynb
+++ b/Chapter01/02_Graph_metrics.ipynb
@@ -1,153 +1,34 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting networkx==2.5\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/cd/dc52755d30ba41c60243235460961fc28022e5b6731f16c268667625baea/networkx-2.5-py3-none-any.whl (1.6MB)\n",
-      "\u001b[K     |████████████████████████████████| 1.6MB 10.0MB/s eta 0:00:01     |███████████████▍                | 778kB 10.0MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from networkx==2.5) (4.4.2)\n",
-      "Installing collected packages: networkx\n",
-      "Successfully installed networkx-2.5\n",
-      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
-      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
-      "Collecting matplotlib==3.2.2\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8d/b5/2309a0308d22cb8955c5140ad47080d990244df626877a86b86cebf153bc/matplotlib-3.2.2-cp38-cp38-macosx_10_9_x86_64.whl (12.5MB)\n",
-      "\u001b[K     |████████████████████████████████| 12.5MB 2.6MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (2.4.7)\n",
-      "Collecting cycler>=0.10 (from matplotlib==3.2.2)\n",
-      "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
-      "Requirement already satisfied: numpy>=1.11 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (1.19.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.1 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from matplotlib==3.2.2) (2.8.1)\n",
-      "Collecting kiwisolver>=1.0.1 (from matplotlib==3.2.2)\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/89/cdd752da48b66f31a0732902cd9800e6d232ce65f6cc4e83f9d9d2bd99aa/kiwisolver-1.3.1-cp38-cp38-macosx_10_9_x86_64.whl (61kB)\n",
-      "\u001b[K     |████████████████████████████████| 71kB 6.6MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: six in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from cycler>=0.10->matplotlib==3.2.2) (1.15.0)\n",
-      "Installing collected packages: cycler, kiwisolver, matplotlib\n",
-      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.2.2\n",
-      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
-      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
-      "Collecting pandas==1.1.3\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/09/3c2ee77531dc30d4265d1f148d08d283de2d57fdd00745a9b367137d54ac/pandas-1.1.3-cp38-cp38-macosx_10_9_x86_64.whl (10.1MB)\n",
-      "\u001b[K     |████████████████████████████████| 10.1MB 5.1MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: numpy>=1.15.4 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from pandas==1.1.3) (1.19.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from pandas==1.1.3) (2.8.1)\n",
-      "Collecting pytz>=2017.2 (from pandas==1.1.3)\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/94/784178ca5dd892a98f113cdd923372024dc04b8d40abe77ca76b5fb90ca6/pytz-2021.1-py2.py3-none-any.whl (510kB)\n",
-      "\u001b[K     |████████████████████████████████| 512kB 9.4MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: six>=1.5 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas==1.1.3) (1.15.0)\n",
-      "Installing collected packages: pytz, pandas\n",
-      "Successfully installed pandas-1.1.3 pytz-2021.1\n",
-      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
-      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
-      "Collecting scipy==1.6.2\n",
-      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/c9/8d034cd5e0543e15e99a35836428c70618b87aff1fb80656094fba23c659/scipy-1.6.2-cp38-cp38-macosx_10_9_x86_64.whl (30.8MB)\n",
-      "\u001b[K     |████████████████████████████████| 30.8MB 2.6MB/s eta 0:00:01     |████████████████████████▌       | 23.6MB 2.4MB/s eta 0:00:03     |███████████████████████████████▏| 29.9MB 2.6MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: numpy<1.23.0,>=1.16.5 in /Users/aldo/.pyenv/versions/3.8.0/lib/python3.8/site-packages (from scipy==1.6.2) (1.19.1)\n",
-      "\u001b[31mERROR: tensorflow 2.2.0 has requirement scipy==1.4.1; python_version >= \"3\", but you'll have scipy 1.6.2 which is incompatible.\u001b[0m\n",
-      "Installing collected packages: scipy\n",
-      "  Found existing installation: scipy 1.4.1\n",
-      "    Uninstalling scipy-1.4.1:\n",
-      "      Successfully uninstalled scipy-1.4.1\n",
-      "Successfully installed scipy-1.6.2\n",
-      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 21.0.1 is available.\n",
-      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install networkx==2.5 \n",
-    "!pip install matplotlib==3.2.2 \n",
-    "!pip install pandas==1.1.3 \n",
-    "!pip install scipy==1.6.2 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "uKraKcP_lyqO"
-   },
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "ci5ithlNjeCM"
    },
    "source": [
-    "## Chapter 2.2: Graph properties"
+    "## Chapter 1.2: Graph properties"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "id": "rNOJ_ofKpb94"
    },
    "outputs": [],
    "source": [
-    "# draw a simple graph\n",
-    "def draw_graph(G, node_names={}, filename=None, node_size=50):\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "    \n",
-    "    if filename:\n",
-    "        plt.savefig(filename, format=\"png\")\n",
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
+    "import os\n",
+    "import sys\n",
     "\n",
-    "# draw enhanced path on the graph\n",
-    "def draw_enhanced_path(G, path_to_enhance, node_names={}, filename=None):\n",
-    "    path_edges = list(zip(path,path[1:]))\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
     "\n",
-    "    plt.figure(figsize=(5,5),dpi=300)\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=50, edge_color='gray')\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
-    "    nx.draw_networkx_edges(G,pos_nodes,edgelist=path_edges, edge_color='#cc2f04', style='dashed', width=2.0)\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "    \n",
-    "    if filename:\n",
-    "        plt.savefig(filename, format=\"png\")"
+    "from utils import draw_graph, draw_enhanced_path"
    ]
   },
   {
@@ -161,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "id": "pnrTW0IDl9ch"
    },
@@ -175,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -200,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -212,9 +93,9 @@
    "outputs": [
     {
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4AAAg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\nG40HAAA8xGazae/evUpPT9fx48ft7oV7VaqL4YIKbCFur2tMv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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x121fbfcf8>"
+       "<Figure size 1500x1500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -236,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -252,7 +133,7 @@
        "2.1904761904761907"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -285,7 +166,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.6111111111111112\n",
+      "0.611111111111111\n",
       "0.6666666666666667\n"
      ]
     }
@@ -302,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -314,9 +195,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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lCIIg/P39idLSUoIgCKK6uprcrqioiDA1NSWePHlCEARBnD59mnzugw8+IGJi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\nArb15OBB7kk5AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1220158d0>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -339,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -351,9 +232,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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LIx9qamoYOXIkADNnziQ0NJSBAwcyd+5cGhsb2blzJ9XV1URERNC9e3dGjhyJXq/n7Nmz\nDBo0iLlz59Kli1yJFubn0kwzqamp3HnnnVqXY7ba3Rnyr/n+++85deoUs2bNAi4+zSoiIoJVq1Y1\nWW/VqlXs2LHDGMYAs2fPZu3atdTU1ACwbds2oqOjMRgMrFq1irvvvhsACwsLRowYwQsvvMDGjRvp\n168fCxcuJC4uTsJYmLURI0aQm5vLqVOntC7FbJnVGXJ2djaurq5NLjy4u7uTnZ1tfH/o0CFWrVrF\njz/+SHl5ufFuuJtvvtk4l5i/vz9BQUHodDoKCwupra3FysqKlJQUDh06hMFgoK6ujvnz58tQINFh\nWFtbExcXx4YNG5g/f77cwNQKzOoM2cfHh5KSEhoaGozLzp07R48ePYzv+/btS0pKCoMHD2bRokVN\nPp+QkMDy5ctZsWIFCQkJGAwGiouLsba2ZuXKldjZ2XHvvffSs2dPeeyl6JD8/f1xcXFBr9drXYpZ\nMqtAjoiIwM/Pj9WrVwNw6tQpvv/+e2bOnGlc59KNGO+88w5r165l69atxrZZs2aRmprKgQMHuHDh\nAm+++SY7d+5k8uTJVFdXM3r0aKytrfnss89ISkpq24MTwgTodDrGjx/Pjh07KC8v17ocs9Pubgy5\n5JFHHmH58uUopZgzZw6vvPIKACdPnuS+++6jsrKShoYGnnrqKSZMmMDWrVtZsGAB+fn5RE6eTmff\nQL5++zmsLGDhgw/y5JNPkp+fT1xcHD4+PsydO5fw8HC8vb2pqKhg4cKFHDt2jIaGBm699VYeeeQR\nOUMWHVZqaiqVlZXccsstWpdiVtptIF+LX96n72BjiQ5YHO1OZdZBSkpKSElJ4b333qNPnz5alyuE\nyaqtreWtt97izjvvbDJbibg+ZtVl8WsqahtITNZTWdtofLxgVV0jlXWNPP7VT5RVVDNr1iw6d+4s\nYSzEb7C1tSU2NpaUlBQMBoPW5ZiNDhPIXx/M5Up/C1gAz73wAhMnTuTRRx9t07qEaK+CgoKwsbFh\n3759WpdiNjpMIGedr2zxwdsA9bYuLErewt69e41Togshfp1OpyM+Pp5t27ZRVSWzidwIHSaQfbs6\n4mDT8rhJmQ1BiGvj4eFBYGBgk9FK4tp1mECeFOzFlQZFyGwIQly7mJgYjh07Rl5entaltHsdJpCd\nbK1ITgzH3kqHje5iZ7KDjSWOtpYkJ4bL9OZCXCN7e3tGjx5NSkoKHWjQVqvoUMPeADan7WB7VgVO\nnr3xdXNgUrCXhLEQ10kpxQcffEB4eDiDBg3Supx2q8MlUXV5KVMCPQkLC9C6FCHMxqULfGvWrMHf\n318eO3uNOkyXxSUlJSW4urpqXYYQZsfb2xs/Pz+2b9+udSntVocMZHlMphCtY+zYsRw8eJBz585p\nXUq71KECubGxkfLyclxcXLQuRQiz5OjoSFRUFBs2bJALfNegQwVyaWkpzs7O8hxXIVpRWFgYlZWV\nHD16VOtS2p0OFcjSfyxE67OwsGDChAls2rSJuro6rctpVySQhRA3nK+vLz4+PuzcuVPrUtqVDhXI\nxcXFckFPiDYSGxtLeno6xcXFWpfSbnSoQJYzZCHaTqdOnRg+fDgbN27UupR2QwJZCNFqhg4dyvnz\n58nMzNS6lHahwwSyUkrGIAvRxqysrJgwYQIbN25sMvmwaFmHCeTKykqsra2xtbXVuhQhOhQ/Pz/c\n3d3ZvXu31qWYvA4TyMXFxdJdIYRG4uLi2L17NxcuXNC6FJPWYQJZ+o+F0I6rqythYWGkpqZqXYpJ\n6zCBLGfIQmhr5MiRZGdnc/r0aa1LMVkdJpBLS0vlgp4QGrK2tiYuLo4NGzbQ2Njy/JYdXYcJZDlD\nFkJ7AQEBODs7s3fvXq1LMUkdJpBlyJsQ2tPpdIwfP54dO3ZQUVGhdTkmp0MEcm1tLbW1tTg5OWld\nihAdnru7O4MGDWLLli1al2JyOkQgXxphobvStNNCiDY1atQoTp48SXZ2ttalmJQOFchCCNNga2vL\n2LFjSUlJwWAwaF2OyZBAFkJoYuDAgVhZWbF//36tSzEZHSKQZYSFEKbn0kzV27Zto7q6WutyTEKH\nCGQZYSGEafL09GTAgAFs27ZN61JMQocJZDlDFsI0jR49miNHjpCfn691KZoz+0A2GAyUlZXRuXNn\nrUsRQrTA3t6emJgYmamaDhDIFy5cwMnJCSsrK61LEUJcweDBg6mvr+fQoUNal6Ipsw9kuaAnhOm7\nNFP15s2bqa2t1boczZh9IEv/sRDtg4+PD3369OHbb7/VuhTNSCALIUzG2LFjycjI4Pz581qXookO\nEcgy5E2I9sHJyYnIyMgOe4HP7ANZ+pCFaF/CwsIoLy/n2LFjWpfS5sw6kC/NNC2BLET7YWlpaZyp\nur6+Xuty2pRZB3JVVRUWFhbY29trXYoQ4jLr1q0jJCQEnU7H6tWrm7V37dqVJ554Ah8fHxYvXsyL\nL77Ic889B8Czzz6Lp6cnzzzzTBtX3frMenCu9B8LYZpuvfVWXF1diY+P5x//+AczZsxo0v7xxx8D\nEBgYyMKFC3FwcDD2KT/99NOcOnWqzWtuC2Z9hiz9x0KYtunTp5Oent5kSielFKmpqYSFheHt7c3G\njRuxtbXFzs5Ow0rbhlkHsvQfC2HaevbsydSpU3njjTeMyzZt2kRsbCw6nY4ePXrw7bff0rdvX6Kj\no6+4nQULFjBmzBiio6P5n//5H8rKygB477338PX1Zfr06cybN4/Q0FDi4+Opqalp7UO7JhLIQghN\nPfDAA6xdu9b4cKHly5eTmJgIXLyDb+HChQwbNuxXh8EFBASwZcsW0tLS8Pf3Z8mSJQD84Q9/IDEx\nkR07dvDSSy+Rnp7O2bNnWbduXasf17Uw6z7k4uJiQkJCtC5DCPErRo0axYABA3jnnXdISEjA09Oz\nyfyX/fv3x9HR0XjW2xI7OzsiIyOxsLCgoKCAPn36NGmPiIgwnpwFBQVx+vTp1jmY62TWgSxnyEK0\nD/fffz9//vOfKSoq4sEHH2zWPnDgQDZv3kxZWRmdOnVq0paWlsZDDz3EoUOH8PX1JTk5meTk5Cbr\nXP4ZOzs76urqWuU4rpfZdlnU19dTXV3d7IcnhDA9M2fOpL6+nqysLPz8/Jq1Ozk54ezsTGpqarM2\nvV6Pv78/vr6+AO167LLZniGXlJTQuXNnmWlaiHbAzs6Ojz76iF69el1xHRcXF86ePcvR46c4fb6S\n7PpzrNl7Fu9evTlx4gRFRUW4ubmxcePGNqz8xjLbQC4uLpYxyEKYqNTUVBYtWkRpaSmOjo4sWrSI\nKVOmGNtnz55NRkYGp0+fxtbWllWrVpGfn499F0+eX5fOhX1bwNKaI+U2dB4cy6i4yURERBAcHIyT\nkxMZGRk88sgjhISEkJycTE1NDW+//TaWlpZ888032NnZ0b9//2bjn7WmU2b6BI/du3dTWlrKhAkT\ntC5FCHEDVNQ2EPHiZiprG5u1Odpaon98LI627fsc02z7kOWCnhDm5euDuVzp9FGpi+3tnVkHsnRZ\nCGE+ss5XUlXX/OwYoKqukayiqjau6MYz20CW26aFMC++XR2xs2r5Ir2DjSW+bg5tXNGNZ5aBbDAY\nuHDhgsw0LYQZCXKupbGhocU2nQ4mBXu1cUU3nlkGcllZGQ4ODlhbW2tdihDiBsjOzmbDV//m1Sl9\ncbS1xMHGErh4Zuxoa0lyYni7v6AHZjrsTfqPhTAfhYWFrFmzhilTpuDv78+YUH++PphLVlEVvm4O\nTAr2MoswBjMNZOk/FsI8lJSUsHLlSsaNG4e/vz8AjrZWTAvrqXFlrcMsuyxkyJsQ7V95eTkrVqxg\n5MiRBAcHa11Om5BAFkKYnOrqalauXElISAjh4eFal9NmzDaQpQ9ZiPaprq6O1atX06dPHyIjI7Uu\np02ZXSArpaQPWYh2qqGhgU8//ZSuXbsybty4DvdwsDYN5N+aaba8vBwXFxd69erF4sWLr2kf1dXV\nADLTtBDtjMFg4IsvvsDW1pbJkyd3uDCGNg7kW2+9lddffx17e3v+8Y9/NGv/+OOPqa+vJyEhgb/8\n5S/XtI9L/ccd8YcpRHullOKrr76itraW2267DQsLs/vj/apoctS/NdPs9ZDHbgrRviil2LRpE+fP\nn2fatGlYWZnlaNyrokkg/9ZMs5eUl5czd+5cRo4cybBhw3j55ZeNEx3u2rWLkSNHMnr0aKKjo/n6\n66+Bi2fI+/btY+jQoYwePZrRo0ezZcsW4OJMAosWLWL48OEMHz6chx9+mPr6ek6cOIGfnx/dunXj\nhRdeAODJJ5/k0UcfBeDDDz/Ew8ODhx56qE2+HyE6kh07dnDq1ClmzJiBjY2N1uVoS7Wxbdu2qcWL\nF6u0tDRlY2Oj8vLylFJKzZgxQ5WXl6tRo0apJ598Uiml1N13363mzJmjlFKqqqpKDRw4UC1fvlwp\npVRYWJjas2ePUkqpjIwM43ovvfSScnNzU4WFhUoppdauXWtse/bZZ9WYMWNUQ0ODamhoUOPGjVPP\nPvusUkqp1NRU1b9/f2OdQ4YMUUFBQcb3d911V+t8IUJ0YN9//7164403VFlZmdalmATNOmoun2n2\n5MmTzWaaNRgMrFq1irvvvhu4eJFu2rRpLFu2DIAuXbqwYsUKCgoKGDRoEEuXLgUgJSWFmJgY3N3d\nAbjlllu49957gYvTi8+ePRtLS0ssLS2ZPXu2cXtRUVHk5eVx4sQJcnJyCA0NJTMzk59//plTp041\nm8VWCHF9Dh48yM6dO0lISMDZ2VnrckyCpj3n999/P++++y6vv/66MTQvOXfuHLW1tcZgBXB3dyc7\nOxuA1atX4+DgQGhoKOPHjyczMxO4eN+7l9d/n/pkZWVFREQEcPEBJVfano2NDbGxsXz11VekpKQw\nffp0IiMjWb9+PV9//TUTJ05snS9BiA4oMzOTTZs2MWvWLBmiehlNA/nXZpp1d3fH1taWc+fOGZed\nO3eOHj16AFBbW8srr7zCmTNniIqKYurUqTQ0NODk5ERZWZnxMw0NDRw4cAAAHx+fK24PYNKkSXz9\n9dfs2rWLyMhIJk6cSEpKCrt372bYsGGt8h0I0dFkZWXx5ZdfMn36dLp166Z1OSZF00C+NNPs888/\n36zNwsKC2bNn8/HHHwMXxxd/9tlnJCUlAXDHHXdQVVWFlZUVI0aMoKGxkWXf/oTT4Ams+896snLy\nAfj0009JTk4GIDExkZUrV9LY2IjBYGDlypXG7QHEx8fz3XffYWFhgbW1NZMmTWLLli3Y29tjaWnZ\nyt+GEOYvNzeXtWvXcvvttzc5GRIXten4kt8z06yTkxOvvfYaCxcuZOTIkTQ0NDBjxgxmzZoFwNSp\nUxk7duzFs+iSMqzH3Mer285Q4xaBY1QNNw2NYYC3G717ePLRRx8BsGjRIi5cuGC8HXP48OE89thj\nxv17eHgQHBzMqFGjAOjXrx/e3t7Exsa21VckhNk6f/48n3zyCZMmTZJrMlfQ7med7ggz0QrR3pWW\nlrJs2TJiYmIICQnRuhyT1e5vh+kIM9EK0Z5VVlayYsUKhg0bJmH8G9p9IHeEmWiFaK9qampYuXIl\nQUFBDB06VOtyTF67D2Tfro7G+bV+yc5KZxYz0QrRHtXX1/PJJ5/Qs2dPoqOjtS6nXWj3gTwp2Isr\nPUeosbEB3c/7qaysbNuihOjgGhsb+eyzz+jcuTPjx4+Xh31dpXYfyE62Vv9/xtmWZqKNwMXRjqVL\nl5Keno7BYNC4WiHMn8FgYN26dVhYWDBlyhQJ49+h3Y+yuKSytuGKM9EWFBSQkpJCQ0MDEydObHIn\nnxDixlFKsX79eoqKipg5c2aHfnLbtTCbQP4tSikOHjzI5s2bL04lPmaMPMReiBts8+bNnD59mtmz\nZ2Nra6t1Oe1OhwnkS6qrq9m6dStHjx5lzJgxxhlMhBDX57vvviMjI4OkpCQcHORi+rXocIF8SW5u\nLikpKVhYWBAfH4+np6fWJQnRbv3www/s3LmTpKQkOnXqpHU57VaHDWS42I2xb98+tm7dysCBA4mJ\niZE/s4T4nQ4fPsw333xDYmIibm5uWpfTrnXoQL6kqqqKzZs3c+LECWJjYwkKCpJuDCGuwokTJ1i3\nbh0JCQlnD40BAAAXu0lEQVTyV+YNIIF8mZ9//pmUlBTs7OyIj49v8uxkIURTZ8+e5dNPP2XatGn0\n7NlT63LMggTyLxgMBvbu3cu3337L4MGDiYqKknm+hPiF/Px8VqxYwa233trsWebi2kkgX0FFRQWb\nNm3i7NmzxMXFERAQIN0YQgBFRUUkJyczfvx4AgMDtS7HrEgg/4asrCxSUlJwcXFhwoQJdOnSReuS\nhNBMWVkZy5YtY+TIkQwZMkTrcsyOBPJVaGxsZM+ePXz33XeEhYUxcuRIrK2ttS5LiDZVVVXFsmXL\nCAkJYcSIEVqXY5YkkH+HCxcusGnTJvLy8pgwYQL9+vXTuiQh2kRtbS3Lly+nd+/ejB07VutyzJYE\n8jU4ceIEGzZsoFu3bsTFxdG5c2etSxKi1dTX17N69Wrc3NyYOHGiXEtpRRLI16ihoYFdu3axZ88e\nhg0bxvDhw2UiVGF2Lj1G09ramttuuw0Li3b/gEiTJoF8nUpKSvjmm28oKioiPj5eJm8UZkMpxb//\n/W+qqqqYPn26nHC0AQnkG+Snn37im2++wdvbm3Hjxsn9/KJdU0qxYcMGCgoKmDVrllzEbiMSyDdQ\nfX09O3bsID09ncjISMLDw+WsQrRL27ZtIzMzkzlz5mBnZ6d1OR2GBHIrKCoqIiUlhYqKCuLj4+nV\nq5fWJQlx1fbs2UN6ejpJSUk4OjpqXU6HIoHcSpRSHDlyhE2bNtG7d29iY2PlP25h8jIyMkhLSyMp\nKQkXFxety+lwJJBbWW1tLdu3b+fAgQNER0czZMgQuVItTNLRo0dJSUlhzpw5dO3aVetyOiQJ5DZS\nWFhISkoKdXV1TJw4EW9vb61LEsLo1KlT/Otf/2LmzJky56SGJJDb0OXz+vXv358xY8bIVDdCc9nZ\n2XzyySfcddddcr1DYxLIGqipqWHr1q0cOXKE0aNHM3jwYLn7SWiisLCQ5cuXM2XKFPr37691OR2e\nBLKG8vLyWL9+PTqdjokTJ8qMC6JNlZSUsGzZMmJjYxk4cKDW5QhAri5pqHv37sydO5fBgwezcuVK\nNmzYQE1NjdZlCQ2lpqYaZ0IfNWoUUVFR9OvXj4SEBCorK2/YfsrLy1mxYgWRkZESxiZEzpBNRFVV\nFVu2bCEzM9N4xiLdGB1TWloaMTEx1NfXY2VlRUlJCQEBAdx7770888wz17396upqkpOTCQwMJCoq\n6voLFjeMldYFiIscHByYPHky2dnZrF+/nv3798u8fgIAV1dXIiMjSU9Pv+5t1dXVsWrVKvr27Utk\nZOQNqE7cSNJlYWJ69OjBPffcw4ABA0hOTiY1NZW6ujqtyxIaa2hooEePHgAcP36c8ePHExUVxfDh\nw9mwYQMAer2ekJAQfH19WbJkCSNGjCA8PJysrCzmz5/PwIEDGT16NO7u7sTGxqLT6Vi+fDlDhw5l\n1KhRzJgxg7KyMi0PUyhhssrLy9UXX3yhXnvtNXX48GFlMBi0Lkm0gW3btilA1dfXK6WUOnPmjJoy\nZYrKzs5W9fX1yt/fXy1btkwppdTx48eVs7OzOnHihPGz1tbWavfu3UoppaZOnaqGDBmiiouL1cqV\nK1WnTp3Ud999p5RSaufOncrNzU0VFhYqpZR6+OGH1dy5c9v4aMXl5AzZhDk5OXHrrbdy2223kZaW\nxqpVqygqKtK6LNFGxowZQ1hYGAEBAcTGxuLt7c3333/PqVOnmDVrFgB+fn5ERESwatUq4+ecnZ0Z\nOnQoAEFBQfTq1Ytvv/0Wg8FAUFAQWVlZACQnJzN58mRjt9iMGTNYtWoVSi4raUYCuR3o1asX8+bN\no0+fPnz44Yds27aN+vp6rcsSrWzLli3s3buX+++/n0WLFlFYWEh2djaurq5YWf338o+7uzvZ2dnG\n987OzsZ/W1paUlZWxvnz55k2bRrW1tbGLrDs7Gy2bt1KdHQ00dHR3H///Xh4eMgvfQ1JILcTlpaW\nDB8+nPnz51NUVMTSpUvJzMzUuizRBhYvXoyzszPvvvsuPj4+lJSU0NDQYGw/d+6csX/5l86ePUtF\nRQUzZszAxsamSZuPjw+TJk0iLS2NtLQ0du7cSXp6ujzHQkMSyO1Mp06duOOOO5g0aRIbN25kzZo1\nlJaWal2WaEUODg48+OCDvP322wwZMgQ/Pz9Wr14NXHwGxffff8/MmTOpqG1g67ECLlTXs2bvWbZ/\nt4f8/Hz69OmDvb19s+0mJiayfv16SkpKgIuTLEyePLlNj038gtad2OLa1dfXq+3bt6uEhATl5+en\nrKys1P79+43tZ86cUaNGjVIuLi5qypQp17yf77//Xg0aNEj16tXrBlQtfs2mTZvUoEGDFKCioqLU\n4cOHlVJKlZaWqk6dOqkhQ4aotLQ0NX78eBUZGamGDRumUlJSlP50keq74B1l69FHYWmtXAaPV93v\neEJ5eHkrDw8PtXTpUvWXv/xFubi4KH9/f7VlyxallFIrVqxQERERKiYmRsXFxamffvpJy8Pv8OTG\nEDNQWlrKa6+9xgsvvEBAQAAHDhxo0scYHR1NWlrade0jLS2NxMRE4wUhYToqahuIeHEzlbWNzdoc\nbS3RPz4WR1u55aA9kC4LM9C5c2dGjx7NnXfeSVZWFgkJCTKetAP5+mAuVzqtUupiu2gf5NemGQkI\nCGDChAncc889eHl5cfvttxMREWFsP3z4MI888gh1dXVUVFSQlJTEH/7wB+rq6hg3bhzbt2/nr3/9\nK9u2bSMnJ4eEhAQee+yxFvdVUVHBAw88QGZmJgaDgdmzZzN//nwAvvzyS1566SUcHBywsLDg2Wef\nZdiwYW3yHZgbg8FAVVUVlZWVLb6qqqr4zxkdVXVOLX6+qq6RrKKqNq5aXCsJZDMzZ84c/v3vf7N9\n+3YGDRpERkaG8YFFFRUVPP3000RERFBfX09wcDAxMTH069ePtLQ0dDodpaWlbNq0ieLiYgIDAwkN\nDWXcuHHN9vOnP/2JxsZGdu7cSXl5OYMGDSIoKIiRI0dyzz33cOjQITw8PPjyyy/ZuHGjBPL/p5Si\npqamxWBtKXBra2uxt7fHwcEBR0fHJi9vb28cHR0p7lzD/l15VNcbmu3PwcYSXzd55nZ7IYFsht55\n5x0CAwMpLCwkPj6e1157jXXr1jFkyBCef/55/vSnP2FjY0NeXh779++nX79+xs9Onz4dgC5duhAf\nH8+aNWuaBbLBYGDFihVs3LgRuDjudfLkyaxYsYKRI0fSpUsX3n//fe677z4mT55MXFxc2x28Burq\n6q4qXC+1WVtbNwtXR0dH3Nzc6NmzZ5Nl9vb2vznlV4/eDby1J7/FNp0OJgXLDCDthQSyGfLw8OCt\nt94iKSmJW265BW9vb5ycnLjttttwdHRk+/btWFtbEx0dTVVV0z9nXV1djf92c3Pj0KFDzbZ/7tw5\namtreeSRR4zDqUpLSwkJCQEuPkLyr3/9KwEBAURGRvLKK6/Qu3fvVjziG6uxsfGqgvXSv5VSzcLV\nwcEBZ2dnPD09my2//ILrjeBka0VyYjiJyXqUuthN4WBjiU4HyYnhckGvHZGflJmaNm0an3/+OXPn\nzkWn0xEbG8t9991HQEAAH33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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12492fd30>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -385,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "id": "1BuMAkinyt8-"
    },
@@ -399,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -415,7 +296,7 @@
        "0.6666666666666667"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -448,7 +329,7 @@
        " 7: 1.0}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -471,9 +352,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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16waFQoF+/foZuywik8RQJDITGo0G586dQ2pqKuzt7aFQKNC/f39OU0X0OwxF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\nxi6n1XJzc9GjRw/Y2prvA/r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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x122000438>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -497,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "id": "VV2e-FNe1kWf"
    },
@@ -511,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -532,7 +413,7 @@
        " 7: 0.3333333333333333}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -543,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -557,18 +438,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -606,15 +487,15 @@
        "Degree centrality  0.333333  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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vvMMbb7xBfn4+xy9cwRT6EK6BoRRlneLi8rexXMqg/n2DaBgcTvaXH1Lf1eDJ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\nmTNnDu7u7sD39+o8PDzIycm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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1257163c8>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -633,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -654,7 +535,7 @@
        " 7: 0.4}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -665,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -679,18 +560,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -725,15 +606,15 @@
        "Closeness centrality     0.4    0.4  0.545455   0.6  0.545455     0.4    0.4"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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7jRg2fVjarbu4XnTPgBURUW0YikRGVqnS4FhGUbPOodUKJJzLNVBFRFQXhiKR\nkZWUq6CQNW9BvUorkHunwkAVEVFdGIpERqYRAkDzd5lRaXhPHJGxMRSJjMzWUgGVRtusc0glgION\nuYEqIqK6MBSJjKyTuRx9HTo16xzmchmCPR0MVBER1YWhSNQCngvqA2szWZPb97C1wPCetgasiIhq\nw1AkMjIhBByqbumfctFYlgoZngvqw6dfELUAhiKREeXm5mLDhg34/Xwa3g3vDwtF4/7KmcmkGOhk\ng+leLkaqkIgexEdHERmBUqlEcnIyzp49i5CQEHh5eUEikUAls8S7CRdQqar/xhtzuRS97a2xKXok\nFDL++5WoJXDvUyIDu3jxIvbs2QM3NzeMHz8e1tbW1Y4fvJiP139IQ0m5ChVKDR7+C2ipkEErBKaN\ncMGKqYNhoWj6XCQRNQ5DkchA7ty5g8TERBQWFiIsLAweHh51vlcIgdTM2/jqUAZO3ShGhVIDhUyC\n7p3MMX+0O556rCdsLRUtWD0RAQxFombTaDQ4fvw4Dh8+DF9fXzz++OOQyzkzQdQWMRSJmiErKwvx\n8fGwtrZGWFgYunbtauqSiKgZ+M9ZoiaoqKjAgQMHcOnSJUyYMAGDBw/mkgmidoA9RaJGEEIgLS0N\nSUlJGDBgAEJCQmBhYWHqsojIQBiKRA1UVFSE+Ph4VFRUIDw8HC4uXDtI1N5w+JSoHmq1GocPH0Zq\naioCAwMxcuRISKVcN0jUHrGnSPQIGRkZiI+Ph6OjIyZOnIjOnTubuiQiMiL2FIlqUVZWhn379iEr\nKwuTJk1C//79TV0SEbUA9hSJHiCEwKlTp3Dw4EF4eXkhMDAQZmZmpi6LiFoIQ5FIJzc3F3FxcZBK\npQgLC4Ojo6OpSyKiFsbhU+rwlEolDh48iLS0NIwdO1a/eTcRdTzsKVKHdvHiRSQmJsLDwwOhoaE1\nNu8moo6FoUgdUklJCfbs2YPCwkKEh4fD3d3d1CURUSvAUKQORaPR4NixYzhy5AhGjRoFPz8/bt5N\nRHoMReowsrKyEBcXBxsbG0yePJmbdxNRDfwnMrV7FRUV2L9/P65cuYLx48dz824iqhN7itRuCSHw\n22+/Yf/+/Rg4cCDGjh3LzbuJ6JEYitQuFRYWIj4+HpWVldy8m4gajMOn1K6o1WqkpKTgxIkT3Lyb\niBqNPUVqN65evYqEhARu3k1ETcaeIrV5ZWVl2Lt3L7Kzs7l5NxE1C3uK1GZptVqcOnUKycnJ8PLy\nQlBQEBTBDlX6AAATVElEQVQKhanLIqI2jKFIbVJOTg7i4+Mhk8kQFhYGBwcHU5dERO0Ah0+pTamq\nqkJycjLS0tIQEhKCESNGcM0hERkMe4rUJgghcPHiRezZswe9e/dGaGgorKysTF0WEbUzDEVq9UpK\nSpCYmIjbt28jLCyMm3cTkdEwFKnVenDz7tGjR8PPzw8ymczUZRFRO8ZVzR3U7t279fNxO3bsqHG8\ntLQUtra2cHNzw1tvvYUPPvgA//jHPwAAK1asQI8ePfD2228brb4bN27g66+/xrVr17Bo0SIEBAQw\nEInI6HijTQc1ffp02NnZYfLkyfjss88QERFR7fjmzZuhUqkwb948vPPOO6iqqsL9QYU333wTGRkZ\nRqmroqICSUlJSE9Px4QJEzBo0CDeSENELYY9xQ5u1qxZOHnyJE6cOKF/TQiBpKQk+Pj46F8zNzc3\n6mbaQgicPXsWX3zxBRQKBZYtW8anWRBRi2ModnC9evXCtGnT8Omnn+pf27dvH0JDQ/WBlJSUhAED\nBiA4OLjO8yxbtgwhISEIDg7G7NmzcffuXQDA119/DXd3d8yaNQtLliyBt7c3Jk+ejMrKSn3bwsJC\nbNmyBcePH0dERAQmTZrEp1kQkUkwFAl//vOfERsbi9zcXADAli1bEBUVpT8eGhqK11577ZHnGDBg\nAA4cOIDk5GR4enpi1apVAIDFixcjKioKKSkp+PDDD3Hy5EncuHEDu3fvhkqlwk8//YQNGzZgwIAB\nWLRoEZydnY12nURE9eGcIiEoKAgDBw7EunXrMG/ePPTo0QOdOnVq1DksLCwQEBAAqVSKvLw89O7d\nu9pxX19f2NnZAQCGDBmCkydPoqCgAE5OTli6dCk37yaiVoGhSACAF154AW+88QaKiorw4osvNqpt\ncnIyli9fjrS0NLi7u2PTpk3YtGlTtffcD73S0lLk5uaipKQES5cuRb9+/Qx1CUREzcbhUwIAzJkz\nByqVCpmZmejbt2+d70vPL8P6w9dw/tYdHM8owrcnbuDnI0fh6empX1SvUqlqtBNCIDU1FevWrYO5\nuTl8fHwYiETU6rCnSAD+GP7csGED3NzcahwTQuC37BKcu3kH4f9MgVYAt3JKIS8vwvW4Cyi5cA9F\nv1/CL+cz4TfYHXv37q3WvrS0FFeuXMH58+cxf/58nD9/nmsOiahVYih2UElJSYiJiUFJSQmsra0R\nExODqVOn6o9HRkbizJkzuHbtGvZdLMLZ5DioyoqhTFgLqZUtKq6dgkRmhrzO9ug0bBzM+vkhOMAP\nXsOHw9PVHmfOnMHy5cshl8uxceNGSKVSVFRU4Pvvv8eePXtgYWGB/v3711gfSURkStzmjeqk1Qos\n2XYKKekFqFRpG9TGQiHF+9OGYIBlKfbu3cvNu4moTWEoUp22HbuO9xJ+R4VK06h2ColAtGM25jwx\nqdbhWCKi1oo32lCthBBYm5ze6EAEAAEJtH0CGIhE1OYwFKlWx6/dRklFzbtIG0ItgJ0ns1Glbnyg\nEhGZEkORavX9mZuoUDYv1E5kFhuoGiKilsFQpFrl3q1EcyabBQSKyqoMVg8RUUtgKFKtmn37FW/f\nIqI2iOsUqRohBAoKCiCvKsUfyda0RzdJJBJ062Ru0NqIiIyNodjBCSFQWFiIa9eu4fr168jMzIS5\nuTkG2bnjiFyOSnXTunwCgI+7nWGLJSIysnYVivd3aTl79iwCAwOh0WhQXFyMJUuW4M9//nOTzjls\n2DD8+9//fuR+oG3J/RDMzMxEZmYmrl+/DjMzM7i5uaF///4YP348bG1tIYTAvz76CbfuVNZ/0oco\nZBJEjHSFuZxbuRFR29LuFu8nJydjzJgxUKlUkMvlOH/+PLy8vBAfH4/Q0NBGn6+kpARdunQxQqUt\nQwiBoqKiaj1BhUIBd3d3uLu7w83Nrc7r23o0E+8nXmz0WkULhRRJfwmCqx13sSGitqVd9RRrM3jw\nYAwdOhR79uxpUii2tUC8H4L3e4IPhmDfvn0xbty4Bl/THF83HLpSgMNXClGpbtg2b5YKGd57YggD\nkYjapHYfisAfjzJSKBRYsWIFkpOTAQBWVlb4+uuv4ezsjB9//BGvvPIKHB0dMXLkSKSkpCAvLw9P\nPvkk1q9fj08++QRRUVG4ePEili1bpj/nwoULqz2h3hQeDMH7PUGZTNakEHyYVCrB57O98PSaBFy6\nK4VSW/dNN1IJYCaX4p2pg/Gkd8+mXg4RkUm1+1BMTk7GhQsX8M033yA1NRUHDhyARCLBpk2b8Oqr\nr2Lr1q2YOnUqbt++jWXLluGrr77CqlWrEBMTg1WrVuHUqVP6c7355ptYsmQJnnnmGeTm5iI6OrrF\nQ1EIgdu3b1frCUqlUnh4eKB3794YO3as/gn3hnD+t7OYZHMTz04Ix/8evo7L+aXQaAVUmj9G3S0V\nMmiFwPhBjlga1AeDnW0N9tlERC2t3YZiSEgINBoNZDIZYmNj4evri5ycHIwZMwZarRZ3796FUqms\n1sbT0xMDBgwAAKxatarGObt27Ypdu3bB19cX7u7u+Ne//mX063gwBO/3BCUSCdzd3fUh2KVLF0gk\nTVs68Si5ubn46aefEB0dje7du2PqCFdcySvFocsFuH1PCXO5FA6dLTBpSA90sTIz+OcTEbW0dhuK\nBw4cgFz+38u7cuUKZs6ciSNHjsDHxwfJyck1enm2to/u5Xz88cdYvXo1xo4dC2dnZ6xYsQJjx441\naN1CCBQXF1frCQKAh4cH3N3dMWbMGKOF4IOqqqqwa9cuTJgwAd27d9e/3s/RBv0cbYz62UREptJu\nQ/Fhp0+fRufOneHj4wPgjznBxiopKcEbb7yB119/Hdu2bcOUKVOQn58Pa2vrJtdVVwjevzs0ODgY\ndnZ2Rg/Bh2uKi4tDr169MGzYsBb7XCIiU+swodi3b18UFxfj8uXL6N+/P/bs2QMAuHG7HCXlSuTc\nqYRG++jVKdHR0di8eTMcHR0RGBgIlUrV6LASQqCkpKRaCAohTBqCD/v111+Rn5+PRYsWmawGIiJT\naFeheH/xPvDHnOKKFSsQFBQEAPD29sbf/vY3jB8/HoOHDkOFrBNu3LyFocFT0NVrPG7Ffw51WTHc\nho1Gwp49GOxsi+XLl+PMmTP48MMPYW9vj9mzZ+PJJ5+Eubk57t69i61bt9b7RPnaQlCr1epDMDAw\nEF27djVpCD4oLy9PP4+oUChMXQ4RUYtqd4v363Mx9y5mf3MMVSotymtZlC6TSKCQSxA5yh3/M2lA\nk8Lq4RDUaDT6EHR3d29VIfigqqoqfPPNNwgMDOSwKRF1SB0qFDMKyjD1iyMoq1LX+15LhQxzfHvh\njbBB9b734RBUq9Xw8PCAm5sb3N3d0a1bt1YZgg8SQmD37t2Qy+WYOnWqqcshIjKJdjV8Wp9lO37F\nPWX9gQgAFSoNth+/gUlDeuAxt67Vjt25c6fatmkqlUrfC/T3928TIfiw06dPIy8vj/OIRNShdZhQ\nPHfrDq4XlTfqOYGVag2++jkDq6bKqvUE74egm5sb/Pz80L179zYXgg/Ky8vDgQMHEBUVxXlEIurQ\nOkwobjh8DcoG7t95nxDAgQs5+PTWzxjY2xXu7u7tIgQfpFQqERsbi/Hjx8Pe3t7U5RARmVSHCcXT\nWSXQNGH61NJMgdAZkRjdp3v9b25jhBCIj4+Hq6srhg8fbupyiIhMTmrqAlpKVSMff3SfRCpp8BMi\n2prTp08jJycHkydPNnUpREStQocJRRuLps2VCQF0bmLb1uz+POLTTz/NeUQiIp0OE4qTh/SAubxp\nlzvEpbOBqzEtziMSEdWuw4RihK9bo9soZBJEjOwFc7nMCBWZBucRiYjq1mFC0d7GHBOH9IBFI3qL\nCqkU8/3cjVeUCZw5c4bziEREdegwoQgAHz05DH0cOjVoGFUhFXjCvhDdLNrH0gvgj3nE/fv3cx6R\niKgOHSoULRQy7FrihzGe9jCXS6GQ1Qw8azMZulqbYevC0fDv0xVbtmxBeXm5Cao1LKVSiV27diE0\nNJTziEREdehQe58+KKu4HFuOZiLutxyUVqphJpOir0MnLAnsjWBPB8ikEgghsH//fqSnp2PevHno\n1KmTqctuEiEEvv/+e0ilUkybNs3U5RARtVodNhQbSgiBQ4cO4dy5c4iMjETnzm3vTtTTp0/j6NGj\nWLRoEczMzExdDhFRq9Whhk+bQiKRIDg4GF5eXti0aROKi4tNXVKj5Ofn6+cRGYhERI/GUGygxx9/\nHKNGjcKmTZtQVFRk6nIa5P56RM4jEhE1DEOxEUaOHIng4GBs3rwZ+fn5pi6nXgkJCXBxccGIESNM\nXQoRUZvAUGwkLy8vhIaGYsuWLcjJyTF1OXU6c+YMbt68yfWIRESNwFBsgqFDhyIsLAzbtm1DVlaW\nqcupIT8/H0lJSZxHJCJqJIZiEw0cOBDTp0/Hzp07ce3aNVOXo/fgPKKDg4OpyyEialMYis3Qt29f\nzJgxA7t27UJ6erqpywHAeUQiouZgKDaTh4cHZs2ahd27d+P33383aS2cRyQiah6GogG4urpizpw5\niI+PR1pamklq4DwiEVHzMRQNxNnZGZGRkUhKSsLp06db9LPv72s6btw4ziMSETUDQ9GAzp49i40b\nN8Lb2xve3t4IDAyEj48PVq5cCZVKVW/71NRUjBgxAu7u7rUe//rrr+Hu7o6oqCj9a5MnT8bKlSvh\n7OzMeUQiombi3qcGlpycjDFjxmD16tXw9fXFgAEDMGfOHMhkMvznP/+BVProf4ckJycjKioKmZmZ\ntR5/++23kZmZiU2bNgEADh8+jDNnzmDx4sUcNiUiaib2FI0kKioKp0+fRlpaGjZu3IiDBw9i27Zt\nBv2MgoICHD16FDNnzmQgEhEZAEPRSDp37oyoqCj8/vvvOHfuHCZMmIDY2FiMGjUKEskfz3G8du1a\nncOl7777LoKDgzFs2DDs3bu3xnGlUonFixfjo48+wtq1awEAy5YtQ5cuXfD3v/8dTz/9NPr374+/\n/e1vRr1OIqL2hKFoRJ06dcL8+fORmZkJIQSuXr2KnTt36o97eHjgk08+qdHu5s2b8Pb2RnJyMtat\nW4cZM2bU2IQ8MTERkZGRCA8P17+2du1ajBgxAr/++iu+++47HDp0CKtWrcKtW7eMd5FERO0IQ9HI\nrKysMG/ePJSVlaG0tBRarbZBbe6vNfTz84ODgwPi4+P1x2/fvo3s7GyEhYXV2n7ChAmQSCRwcnJC\nt27d6pyfJCKi6hiKLcDCwgKWlpbo3r17rUOhD7Ozs6v2c7du3fSbj9+7dw+3bt3CjBkz6pxHfPBB\nyBYWFlAqlc2onoio42AotoCcnBzs378fzz//vL6neO/ePQBASUlJjfc//CDjwsJCODk5QaVS4cKF\nC3BycoKjo6PxCyci6mAYikZ2+/ZtREdHIzg4GNHR0Vi0aBHMzc2xZs0aqFQqJCYmVnt/cbkSpaWl\nWL3+/5CeX4qUlBQUFBQgLCwMCQkJ6NSpE7p162aiqyEiat/kpi6gPUlKSkJMTAwAICQkBEIIlJeX\nY8aMGVi+fDmkUinMzc2xatUqvP/++9i9ezdmzJiB3NxcBE+cCvPHpuHQ+vch72yPDzd+j7+veB+S\nqntYtuJzXL52Azt37sTx48dRWVmJ9957D2ZmZtizZw8sLCzg6uqKS5cu4cyZM/jwww/h6emJrVu3\nIjc3Fy+99BJ27NiBQYMGmfgbIiJq3bh430S0Wi3i4uKQk1+AVPlQHL12G+VKTa3vtZRLIdFUYXOk\nF3wGuLVwpUREHQdD0YQ0Gi2mrU7ExWIt1PWMZEsA2FopkPjnADjZWrZMgUREHQznFE3oP7/l4GqZ\nrN5ABAABoLRSjZhdvxm/MCKiDoqhaEJrD6WjQlX7kGltNFqBE5m3caukwohVERF1XAxFE/k95y6y\nbjc+3IQQ2HbsuhEqIiIihqKJXMy9C6mk8e2UGoHTWTXXNhIRUfMxFE2kSq2Ftom3OFU2YsiViIga\njqFoIraWCsia0lUEYGfFx0QRERkDQ9FE/Pp0h0pT/+bgD7M2k2HaCGcjVERERAxFE7G1VGDC4B6N\nnleUSCSYOKSHcYoiIurgGIomtCy4D8zkDf8jsFTI8GyAB8zlMiNWRUTUcTEUTWhAj85Y9dRwWCjq\n/2OwVMgQ1N8eL4zp1wKVERF1TNzmrRVIvpSPv3x3Fkq1Bvce2v/UUiGDVghEjnLD/0waCGkTb84h\nIqL6MRRbCY1WIPlSPv738DVkFt2DSqNFVyszzPRxxdOPucLWUmHqEomI2j2GIhERkQ7nFImIiHQY\nikRERDoMRSIiIh2GIhERkQ5DkYiISIehSEREpMNQJCIi0mEoEhER6TAUiYiIdBiKREREOgxFIiIi\nHYYiERGRDkORiIhIh6FIRESkw1AkIiLSYSgSERHpMBSJiIh0GIpEREQ6DEUiIiIdhiIREZEOQ5GI\niEiHoUhERKTDUCQiItJhKBIREekwFImIiHQYikRERDoMRSIiIh2GIhERkQ5DkYiISIehSEREpMNQ\nJCIi0mEoEhER6TAUiYiIdBiKREREOgxFIiIiHYYiERGRDkORiIhIh6FIRESkw1AkIiLSYSgSERHp\n/H8l0lULIsvgIwAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x122010780>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -752,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -773,7 +654,7 @@
        " 7: 0.0}"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -784,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -798,18 +679,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -844,15 +725,15 @@
        "Betweenness centrality     0.0    0.0  0.533333   0.6  0.533333     0.0    0.0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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GmTNnam0ze/ZsAECXLl0wYcIEbNmypc7+NRoNNm3ahEWLFgG4N6Zv8uTJ+M9//oMtW7ag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\nWgxFIiIirf8P8gd+pqCWs0MAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1253daa20>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -880,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -896,7 +777,7 @@
        "-0.6"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -919,9 +800,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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EY9Dnn38OpVIJiUSCxMREJCUlXfZvJJ07dw7R0dG3NAKzoqIC77zzjuGa4r/+9S8EBgbe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\niIj0/g9TPqEjaygBIgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1254e8710>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -940,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -953,10 +834,10 @@
     {
      "data": {
       "text/plain": [
-       "-0.75"
+       "-0.7500000000000001"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1021,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1037,7 +918,7 @@
        "0.5454545454545454"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1045,13 +926,6 @@
    "source": [
     "nx.transitivity(G)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1062,9 +936,9 @@
    "toc_visible": true
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap1",
    "language": "python",
-   "name": "python3"
+   "name": "chap1"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1076,9 +950,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Chapter01/03_Graphs_Benchmarks.ipynb b/Chapter01/03_Graphs_Benchmarks.ipynb
index aa98f16..0d644af 100644
--- a/Chapter01/03_Graphs_Benchmarks.ipynb
+++ b/Chapter01/03_Graphs_Benchmarks.ipynb
@@ -12,93 +12,19 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "import networkx as nx\n",
-    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"./figures\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def draw_graph(G, node_names={}, filename=None, node_size=50, layout = None):\n",
-    "    pos_nodes = nx.spring_layout(G) if layout is None else layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')\n",
-    "  \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "  \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
-    "  \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "  \n",
-    "    if filename:\n",
-    "        plt.savefig(os.path.join(output_dir, filename), format=\"png\")\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
     "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
     "\n",
-    "# draw enhanced path on the graph\n",
-    "def draw_enhanced_path(G, path_to_enhance, node_names={}, filename=None, layout=None):\n",
-    "    path_edges = list(zip(path,path[1:]))\n",
-    "    pos_nodes = nx.spring_layout(G) if layout is None else layout(G)\n",
-    "    \n",
-    "    plt.figure(figsize=(5,5),dpi=300)\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=50, edge_color='gray')\n",
-    "  \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "  \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')\n",
-    "    nx.draw_networkx_edges(G,pos_nodes,edgelist=path_edges, edge_color='#cc2f04', style='dashed', width=2.0)\n",
-    "  \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "  \n",
-    "    if filename:\n",
-    "        plt.savefig(os.path.join(output_dir, filename), format=\"png\")"
+    "from utils import draw_graph, FIGURES_DIR, DATA_DIR"
    ]
   },
   {
@@ -117,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,14 +54,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1080x432 with 3 Axes>"
+       "<Figure size 1500x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -153,12 +79,12 @@
     "plt.subplot(1,3,3)\n",
     "plt.title(\"Barbell\")\n",
     "draw_graph(barbell)\n",
-    "plt.savefig(os.path.join(output_dir, \"SimpleGraphs.png\"))"
+    "plt.savefig(FIGURES_DIR / \"SimpleGraphs.png\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,14 +132,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -240,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,14 +175,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -276,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -295,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,14 +240,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1080x432 with 2 Axes>"
+       "<Figure size 1500x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -337,24 +263,22 @@
     "plt.plot(x, y, 'o'); plt.xscale(\"log\"); plt.yscale(\"log\")\n",
     "plt.xlabel(\"Degree k\")\n",
     "plt.ylabel(\"P(k)\")\n",
-    "plt.savefig(os.path.join(output_dir, \"Barabasi_Albert.png\"))"
+    "plt.savefig(FIGURES_DIR / \"Barabasi_Albert.png\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1080x432 with 1 Axes>"
+       "<Figure size 1500x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -393,14 +317,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -409,7 +333,7 @@
    ],
    "source": [
     "nx.draw_kamada_kawai(graph, with_labels=True, node_size=20, font_size=14)\n",
-    "plt.savefig(\"Florentine.png\")"
+    "plt.savefig(FIGURES_DIR / \"Florentine.png\")"
    ]
   },
   {
@@ -436,7 +360,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from zipfile import ZipFile\n",
+    "from io import BytesIO\n",
+    "import shutil\n",
+    "\n",
+    "url = 'https://nrvis.com/download/data/ca/ca-AstroPh.zip'\n",
+    "filename = DATA_DIR / \"ca-AstroPh.mtx\"\n",
+    "tmp_dir = DATA_DIR / \"tmp\"\n",
+    "\n",
+    "r = requests.get(url, allow_redirects=True)\n",
+    "\n",
+    "myzip = ZipFile(BytesIO(r.content))\n",
+    "\n",
+    "tmp_file = myzip.extract(filename.name, path=tmp_dir)\n",
+    "\n",
+    "with open(tmp_file, \"r\") as fid_in:\n",
+    "    with open(filename, \"w\") as fid_out:\n",
+    "        fid_out.write(\"%\")\n",
+    "        fid_out.write(fid_in.read())\n",
+    "\n",
+    "shutil.rmtree(tmp_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -445,26 +398,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "file = \"ca-AstroPh.mtx\"\n",
-    "adj_matrix = mmread(file)"
+    "adj_matrix = mmread(filename)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "graph = nx.from_scipy_sparse_matrix(adj_matrix)"
+    "graph = nx.from_scipy_sparse_array(adj_matrix)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -473,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -482,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -577,7 +529,7 @@
        "4  6.722036e-07  1.000000       2"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,19 +547,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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xeySbid/CMCZ3ooDErSslnVWOYTkpx9wplazWofq5PjXqk7hkbRKI3evkeXIXkUtF5DERedrrcxN55TebbmxI5FI57pd2Ozha7X/6xqYbsWagDx3R6ZyQSImcUBWRxwF8EsDbqvqRquOrAHwdQAbAt1V1k6q+DuA2JneKOj8TuRkvts2zSvD9F8/BM3sLnq1qpUZxm1B1e+f+BIBV1QdEJAPgmwA+DuByACMicrmn0REliFntutm2ea3wsl0BNYpjp01Xd+6q+qKILKw7fDWAVyt36hCR7wK4CcAv3ZxTRG4HcDsA9PWZN3EiakX9JtCDi+Zgy9prWjpXdXVLT1cWqsCJYqmm0sVtBYybbfNajS1+033xMvrpK2M36S3qsn91Jbl/zxiWEZFbAKxS1b+pfP15ACsA3A/gYQA3YGqoZqPTufv7+3VsbKyV+Ilq1Cd2QysJvr66pV4um8HNV/U2DIfkshlsXL3M12TgFBt5p7cnh13rrw87DFMisldV+80e87wUUlXfAXCH1+clcsMssdsdt+M01FEslae7StYf97vvN4dhguHVsFkY2qmWKQCobn49v3LMNREZEpHNJ06caCMMIn+4qY6w2rnJ78qKuFVuxI3R8sHvv8D81M6d+8sAlojIJZhK6p8F8LlmTqCqOwDs6O/vX9tGHES+cLP7UUbENMG7rawwG68HgAeeO4iJYgkAMLsri/uHrsBTY4db+guEmpMRwWsbPxF2GG1zdecuIlsBvARgqYi8JSK3qeoZAF8CsBPArwBsV9WD/oVK5GxwkXlPdqvjdpw6M+ayGQxcOtv0sesum+t4frO2w+ue2o+7t++bTuwA8O6pEu7ato+JPSBR2Y2rXa6Su6qOqOqfqGpWVeer6mOV48+r6p+q6iJVfdjfUImcbVl7TUMib7Vapr4z4+yuLHpy2Zo/2d94x/zO3k17WLNx89KkYtJkpIfVMMFYM9AXmd242hVqbxkRGQIwtHjx4jDDoIRptezRTH1nxnp3bdtnetzNmDjHzaNFgMQkdqCJUkg/sRQy/trpduhlXXqrWo1/cNOPTcfle3ty6OrswP+9fbLmeFe2A8XSJC7qyeHkB2dqhl8oGrqyHfjK6o/GYiLVrhSSjcOobe1sWWdWl77rteO49dGXfIq2UTvxW606PVMuNyR2ADhVmpy+xnvvM7FH0anSJO7evi8yWy62ismd2tZOt0Mv69Jb1U78ZjsmbVy9DL/7w2nHnzUbW6domNT4dYGsxzF3alu73Q7D1m78ZuPyd1qMxVN8xOX9ayXUO3dV3aGqt3d3d4cZBrXJqqY7Ll304h4/+SPurz93YkqhDfkD08vmMyIYWbHAsUrAbsJx3cqlDX1OMh2CoxNF023eqidMBxfNsewF48eWdGbnvO6yudiy+3BDuWGhKv7zzsng5w+uajyhhSUXzjQdc6d46BDEtu2AgdUyKbMhfwBP7j7ccNyuvtesSVV9c6zqpNnVmcHJ0/Z9T6oTvFm1zKf7+xyv2Syz55HtEECAkott1JpN8Dd87SdM8DGUlGoZJveUWXTv86bL5e2WXNuV+5l1y7O6Rj2rjSdauaYbVudshl3MTrhZdbS189qGJbKlkGwcFjyrpGuXjJudcHST2J34MUkb9wkyomZwQjVlMmK+0abVcaD5CUe7c7nlxyRn3CfIiJrBOveUsWqKZNcsqdnt4dw0XnJq5OXHlnRm58x2CLIZd/8YnXeOdRMxN7h/dXS10lgu6lgtEwCrqg8/qkGcGJOmzVTLuN0erroKR2Df7GrXa8dxw9d+gh/dfa3l72HszeM1cd58lX2fFyfGz1bXoJcqK4l6ctma7fPWP70f71dNsp6bkZrJVLPJ0l6T38tH7/8h3vuAm2pEWRjtLoLACVWfWVWahLU9m1+cqnCsKkfmzerEe++XA/v9rHj4R5arR//pM382/Y+uXaWOXRVM9fe1m9gHF83BT99411UlDzUnmxGM3hK/fVHrRXZCNQ2slrZv3XOk5SXvUbR1zxHb41bJ8Hd/OB3o78euLYBxbqd2BHbljdXf1+4d+67XjjOx+6RU1th+1txitYzPmq0oiWtFRytVOK2cz8/fj3Hudit14voapk3SXydWy/is2YqSuFZ0tFKF08r5/Pz9GOdut1Inrq9h2iT9deKwjM+sqj5GVizwvBokTE5VOEsunGn6+LxZnYH+fubN6rR8zDi3U6WO1XOp/752q2sGF81xXclDzclmJLafNbeY3H1m1RL2oeFlpsfjOsHz0PAyrBnom77jzojUtDT40d3XNiTFJRfOxJ77bgj097PnvhsaErzg7GQqYP2aGY+bPReYfN/PH1zlmODPmdHRUCJp/O62rL0Go7dcidld2ZaeK5mb3ZVNxGSqE1bLxEx9pcaSC2fiR3df29Q5Wmkc5he35aD1/WfmzerE7/9YMn0OTufckD9g2iis2rxZnZiRyTS0K1hy4UysuPR808ogio55szobJs+TtD+qgb1lEsKqBK+ZBN9K4zC/uGlIBpjv1mRmzUAf+i+eY3tOq+dP6ZC0BM9SyISwKsFrpvOgU8likNzugOR2V6ate444njOM50nRkabXn6WQKeN1yWI7vG4OVlZ1PGcYz5OiI02vP0shU8brksV2eN0cLCPieM4wnidFR5pefw7LxIhVCZ5daV69VhqH+cVtczC3TZ1GVixwPGcYz5OiI02vP5N7jFiVEzZTLeNUshgkp5JDw5a11zQk+HmzOk2fg9M5jefvdP82b1Ynek3+Clhy4UysGehr9SlTQMzWMyRtMtUJq2WIiGKK1TJERCmTyH7uYfRJj1M8ZhtSt9vPur6V7rxZndhz3w2O13J63GzR1opLz8d39hzGpM0fncaf4HYtfinZjD0FzPrsp0HihmXcLowJStTisVoQ1E6Ct0qgMwQ4Y/L2Mq7lFItd33Q3zs1IzYYblF5x3ivBTqqGZdwujElrPFYLgtwuFDJjdWdsltirr+UUSzuJHQATO02L814JrUrcIiavF8a0K2rxEKVV2j5ziVvE5PXCmHZFLR6itErbZy5xwzJuF8akNR6rBUHt7P5u1SN9hkUxuXEtp1iaWZxl5lz2QqeKOO+V0KrEJXe3C2PSGo/ZgqB2q2XMeqTPm9WJVzfeaHstp1isFm2tGehDh0PeXjPQh1ce/oTt5hyUbMZbJOzPXFgSVy1DRJQWqaqWISIiJnciokRiciciSiAmdyKiBGJyJyJKoNg2DtuQP4Cte46grIqMCEZWLJju1Wz3WJS4aeAV1edS3wxt4fk57H793ek4By6djTfeKTY0S9uQP4Atuw+jukZrZmcGp06Xp8/TTCuE3p4cujo7aloVdAgamooZZXHh14ZRENLaLKxaLEshrXawNzZRsHosCknR4KaBl93zDPO5mDVDc5LLZvCxvu62etgQNSOpzcKqJa4U0moH8617jtg+FiVuGnhF9bmYNUNzUiyVmdgpUGlsFlYtlo3DrHYwL6vaPhY3UX0uaWvARPGV5vdqLBuHWe1gnhGxfSxuovpc0taAieIrze/VWA7LWO1gPrJige1jUeKmgVdUn4tZMzQnuWymreZkRM1KY7OwarFM7sYO9sYdbEZkepLR7rEocdPAK6rPxawZ2uCiOTVxDi6a09Asbcvaa7BmoA/1f3fM7MzUnKcZvT25huZiZk3FBGi4LiVXWpuFVYtltQwRESWwWoaIiOwxuRMRJRCTOxFRAjG5ExElEJM7EVECMbkTESUQkzsRUQIxuRMRJRCTOxFRAjG5ExElEJM7EVECMbkTESUQkzsRUQIxuRMRJdAMr08oIjMB/AuA0wB+oqpbvL4GBSM/XsDozkM4OlHERS52k7fa9BuY6vFeVp3elX7szePYuudIzZaB1TvW11/7usvm4nv7f4uJYsl1/F3ZDogITp4uT399qjTp+ucpuoz3U/XXA5fOxhvvFF2/X5POVT93EXkcwCcBvK2qH6k6vgrA1wFkAHxbVTeJyOcBTKjqDhHZpqqfcTo/+7lHT368gHufPVCzEbbdbvJ2ib1epkNQnjR/3+WyGdx8VS+e2VtoehNuomp279ek8KKf+xMAVtWdNAPgmwA+DuByACMicjmA+QCOVL6Nn86YGt15qCG52u0m7zaxA7BM7MY1tu45wsRObbN7v6aBq+Suqi8CqP/0Xg3gVVV9XVVPA/gugJsAvIWpBG97fhG5XUTGRGTs2LFjzUdOvrLaNT6I3eTLEdgdjJIhiPdrVLUzodqLs3fowFRS7wXwLICbReRbAHZY/bCqblbVflXtnzt3bhthkB+sdo0PYjd5Yy9WonYF8X6NKs+rZVT1pKp+QVX/lpOp8bVu5VLkspmaY3a7yTezsXXGbAfrqmuMrFjQcG2iZtm9X9OgneReALCg6uv5lWOuiciQiGw+ceJEG2GQH4aX92Lj6mXo7clB4Lyb/Ja119gmeONuvLcnh69++kqsGehruEM3rvHQ8LKGa68Z6ENPLtvUc+jKdmBmZ6bma0qG+vdORgSDi+a4fr+mgatqGQAQkYUAvmdUy4jIDAC/BvDnmErqLwP4nKoebDYIVssQETWv7WoZEdkK4CUAS0XkLRG5TVXPAPgSgJ0AfgVgeyuJnYiIvOdqEZOqjlgcfx7A855GREREbQt1EJJj7kRE/gg1uavqDlW9vbu7O8wwiIgSh+UDREQJ5LpaxtcgRI4BeDPsOOpcAOD3YQfhEmP1B2P1B2P1zsWqaroKNBLJPYpEZMyqxChqGKs/GKs/GGswOCxDRJRATO5ERAnE5G5tc9gBNIGx+oOx+oOxBoBj7kRECcQ7dyKiBGJyJyJKICZ3IqIEYnJ3SUQuFZHHROTpuuMzK9sFfjKs2OqZxSoiwyLyqIhsE5G/DDO+ahaxzhSR/6jEe2uY8ZkRkT4RyYvI4yKyPux47IhIh4g8LCLfEJG/DjseJ1H8PJmJ6uepWiqSe+VD+LaI/KLu+CoROSQirzp9SCt7xd5m8tA/ANge9VhVNa+qawHcAeAzUY4VwGoAT1fi/ZQXsXoZM4Bllfi+CGC5l/H5EOtNmNpIp4SprTCjHCvg8efJjEfvW88/T15z1fI3AZ4A8M8A/tM4ICIZAN8EcAOm3vQvi8hzADIANtb9/BdV9e36k4rIDQB+CeDcqMdaZUPlXFGOdT6AA5X/L3sUq2cxA9gN4GkR+SKA//I4Pq9jXQrgf1X13yp/Hf1PhGO9Et5/nnyJtep96+XnyVOpSO6q+mJlJ6lqVwN4VVVfBwAR+S6Am1R1IwC3fxJeC2AmgMsBFEXkeVWdjGKsIiIANgH4gar+rJ0Y/Y4VUx+u+QD2weO/Lr2IWUT+HsD9lXM9DeDfvYzR41jfAnC68qXX/1B6Heu18Pjz5GOsnn+evJaKYRkLvQCOVH39VuWYKRE5X0T+FcByEbkXAFT1PlW9E8B3ADzqxxvRq1gBfBnAXwC4RUTu8ClOwJtYnwVws4h8C8AO3yI9q6mYAfwQwN9V4n7Dx7jMNBvrswBWisg3ALzoZ2Ammoo1wM+TmWZ/r0F9nlqWijt3L6jqO5gaXzN77Ilgo7FnFquqPgLgkXAismYR60kAXwgnImeq+gsAt4QdhxuqegqA2VxRZEXt82Qmqp+namm+cy8AWFD19fzKsShirP6KU8yM1R9xitWVNCf3lwEsEZFLRKQTwGcBPBdyTFYYq7/iFDNj9UecYnVHVRP/H4CtAH6Ls+Vgt1WOfwLArwG8BuC+sONkrIyZsTJWr/5j4zAiogRK87AMEVFiMbkTESUQkzsRUQIxuRMRJRCTOxFRAjG5ExElEJM7EVECMbkTESUQkzsRUQL9P7Yyv42jYXv2AAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -619,19 +569,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -657,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -697,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -710,14 +658,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1080x432 with 3 Axes>"
+       "<Figure size 1500x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -730,7 +678,7 @@
     "    plt.subplot(1,3,ith+1)\n",
     "    plotSubgraph(graph, idx[title])\n",
     "    plt.title(title)\n",
-    "plt.savefig(os.path.join(output_dir, \"PhAstro\"))"
+    "plt.savefig(FIGURES_DIR / \"PhAstro\")"
    ]
   },
   {
@@ -749,11 +697,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
-    "nx.write_gexf(graph, 'ca-AstroPh.gexf')"
+    "nx.write_gexf(graph, DATA_DIR / 'ca-AstroPh.gexf')"
    ]
   },
   {
@@ -765,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -791,21 +739,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "B = nx.Graph()\n",
     "\n",
     "B.add_nodes_from(bottom_nodes, bipartite=0)\n",
-    "B.add_nodes_from(top_nodes, bipartite=1)\n"
+    "B.add_nodes_from(top_nodes, bipartite=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -823,14 +769,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -846,9 +792,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ml-book-1",
+   "display_name": "chap1",
    "language": "python",
-   "name": "ml-book-1"
+   "name": "chap1"
   },
   "language_info": {
    "codemirror_mode": {
@@ -860,9 +806,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/Chapter01/poetry.lock b/Chapter01/poetry.lock
new file mode 100644
index 0000000..5535ea4
--- /dev/null
+++ b/Chapter01/poetry.lock
@@ -0,0 +1,1542 @@
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
+[[package]]
+name = "certifi"
+version = "2023.11.17"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
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+
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+description = "Foreign Function Interface for Python calling C code."
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+python-versions = ">=3.8"
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diff --git a/Chapter01/pyproject.toml b/Chapter01/pyproject.toml
new file mode 100644
index 0000000..a27a361
--- /dev/null
+++ b/Chapter01/pyproject.toml
@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "Graph Machine Learning - Chapter 1"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
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+
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+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/Chapter01/requirements.txt b/Chapter01/requirements.txt
new file mode 100644
index 0000000..709d589
--- /dev/null
+++ b/Chapter01/requirements.txt
@@ -0,0 +1,57 @@
+appnope==0.1.3 ; python_version >= "3.9" and python_version < "3.10" and platform_system == "Darwin"
+asttokens==2.4.1 ; python_version >= "3.9" and python_version < "3.10"
+certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.10"
+cffi==1.16.0 ; python_version >= "3.9" and python_version < "3.10" and implementation_name == "pypy"
+charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.10"
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.10" and sys_platform == "win32"
+comm==0.2.0 ; python_version >= "3.9" and python_version < "3.10"
+contourpy==1.2.0 ; python_version >= "3.9" and python_version < "3.10"
+cycler==0.12.1 ; python_version >= "3.9" and python_version < "3.10"
+debugpy==1.8.0 ; python_version >= "3.9" and python_version < "3.10"
+decorator==5.1.1 ; python_version >= "3.9" and python_version < "3.10"
+exceptiongroup==1.2.0 ; python_version >= "3.9" and python_version < "3.10"
+executing==2.0.1 ; python_version >= "3.9" and python_version < "3.10"
+fonttools==4.47.0 ; python_version >= "3.9" and python_version < "3.10"
+idna==3.6 ; python_version >= "3.9" and python_version < "3.10"
+importlib-metadata==7.0.0 ; python_version >= "3.9" and python_version < "3.10"
+importlib-resources==6.1.1 ; python_version >= "3.9" and python_version < "3.10"
+ipykernel==6.27.1 ; python_version >= "3.9" and python_version < "3.10"
+ipython==8.18.1 ; python_version >= "3.9" and python_version < "3.10"
+jedi==0.19.1 ; python_version >= "3.9" and python_version < "3.10"
+jupyter-client==8.6.0 ; python_version >= "3.9" and python_version < "3.10"
+jupyter-core==5.5.1 ; python_version >= "3.9" and python_version < "3.10"
+kiwisolver==1.4.5 ; python_version >= "3.9" and python_version < "3.10"
+matplotlib-inline==0.1.6 ; python_version >= "3.9" and python_version < "3.10"
+matplotlib==3.8.2 ; python_version >= "3.9" and python_version < "3.10"
+nest-asyncio==1.5.8 ; python_version >= "3.9" and python_version < "3.10"
+networkx==3.2.1 ; python_version >= "3.9" and python_version < "3.10"
+numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.10"
+packaging==23.2 ; python_version >= "3.9" and python_version < "3.10"
+pandas==2.1.4 ; python_version >= "3.9" and python_version < "3.10"
+parso==0.8.3 ; python_version >= "3.9" and python_version < "3.10"
+pexpect==4.9.0 ; python_version >= "3.9" and python_version < "3.10" and sys_platform != "win32"
+pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.10"
+platformdirs==4.1.0 ; python_version >= "3.9" and python_version < "3.10"
+prompt-toolkit==3.0.43 ; python_version >= "3.9" and python_version < "3.10"
+psutil==5.9.7 ; python_version >= "3.9" and python_version < "3.10"
+ptyprocess==0.7.0 ; python_version >= "3.9" and python_version < "3.10" and sys_platform != "win32"
+pure-eval==0.2.2 ; python_version >= "3.9" and python_version < "3.10"
+pycparser==2.21 ; python_version >= "3.9" and python_version < "3.10" and implementation_name == "pypy"
+pygments==2.17.2 ; python_version >= "3.9" and python_version < "3.10"
+pyparsing==3.1.1 ; python_version >= "3.9" and python_version < "3.10"
+python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.10"
+pytz==2023.3.post1 ; python_version >= "3.9" and python_version < "3.10"
+pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.9" and python_version < "3.10"
+pyzmq==25.1.2 ; python_version >= "3.9" and python_version < "3.10"
+requests==2.31.0 ; python_version >= "3.9" and python_version < "3.10"
+scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.10"
+six==1.16.0 ; python_version >= "3.9" and python_version < "3.10"
+snap-stanford==6.0.0 ; python_version >= "3.9" and python_version < "3.10"
+stack-data==0.6.3 ; python_version >= "3.9" and python_version < "3.10"
+tornado==6.4 ; python_version >= "3.9" and python_version < "3.10"
+traitlets==5.14.0 ; python_version >= "3.9" and python_version < "3.10"
+typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.10"
+tzdata==2023.3 ; python_version >= "3.9" and python_version < "3.10"
+urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.10"
+wcwidth==0.2.12 ; python_version >= "3.9" and python_version < "3.10"
+zipp==3.17.0 ; python_version >= "3.9" and python_version < "3.10"
diff --git a/Chapter02/01_embedding_examples.ipynb b/Chapter02/01_embedding_examples.ipynb
index c2f734e..7c3e93c 100644
--- a/Chapter02/01_embedding_examples.ipynb
+++ b/Chapter02/01_embedding_examples.ipynb
@@ -1,35 +1,24 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter2 - Embeddings "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "def draw_graph(G, pos_nodes, node_names={}, node_size=50, plot_weight=False):\n",
-    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray', arrowsize=30)\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "    \n",
-    "    \n",
-    "    if plot_weight:\n",
-    "        pos_attrs = {}\n",
-    "        for node, coords in pos_nodes.items():\n",
-    "            pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "        nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "        edge_labels=dict([((a,b,),d[\"weight\"]) for a,b,d in G.edges(data=True)])\n",
-    "        nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels)\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])"
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import draw_graph"
    ]
   },
   {
@@ -41,22 +30,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing transition probabilities: 100%|██████████| 18/18 [00:00<00:00, 1941.31it/s]\n",
-      "Generating walks (CPU: 1): 100%|██████████| 10/10 [00:00<00:00, 13.25it/s]\n"
+      "/home/euler/.conda/envs/chap2/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Computing transition probabilities: 100%|██████████| 18/18 [00:00<00:00, 18850.80it/s]\n",
+      "Generating walks (CPU: 1): 100%|██████████| 10/10 [00:00<00:00, 53.03it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -68,7 +59,7 @@
     "from node2vec import Node2Vec\n",
     "\n",
     "G = nx.barbell_graph(m1=7, m2=4)\n",
-    "draw_graph(G, nx.spring_layout(G))\n",
+    "draw_graph(G, layout=nx.spring_layout)\n",
     "\n",
     "node2vec = Node2Vec(G, dimensions=2)\n",
     "model = node2vec.fit(window=10)"
@@ -76,19 +67,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -113,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,19 +112,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -160,9 +147,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import random\n",
     "import matplotlib.pyplot as plt\n",
@@ -189,13 +187,32 @@
     "    ax.scatter(vec[0],vec[1], s=1000)\n",
     "    ax.annotate(str(i), (vec[0],vec[1]), fontsize=40)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap2",
    "language": "python",
-   "name": "python3"
+   "name": "chap2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/Chapter02/poetry.lock b/Chapter02/poetry.lock
new file mode 100644
index 0000000..e817f46
--- /dev/null
+++ b/Chapter02/poetry.lock
@@ -0,0 +1,1790 @@
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
+files = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
+[[package]]
+name = "cffi"
+version = "1.16.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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+[package.extras]
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+
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diff --git a/Chapter02/pyproject.toml b/Chapter02/pyproject.toml
new file mode 100644
index 0000000..b6c4916
--- /dev/null
+++ b/Chapter02/pyproject.toml
@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "Graph Machine Learning - Chapter 2"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.poetry.dependencies]
+python = ">=3.9"
+ipykernel = ">=6.0.0" 
+scipy = ">=1.11.0"
+networkx = ">=3.2.0" 
+matplotlib = ">=3.6.0"
+node2vec = ">=0.3.3"
+karateclub = ">=1.0.19"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/Chapter02/requirements.txt b/Chapter02/requirements.txt
new file mode 100644
index 0000000..a36db7e
--- /dev/null
+++ b/Chapter02/requirements.txt
@@ -0,0 +1,63 @@
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+cycler==0.12.1 ; python_version >= "3.9"
+debugpy==1.8.0 ; python_version >= "3.9"
+decorator==4.4.2 ; python_version >= "3.9"
+exceptiongroup==1.2.0 ; python_version < "3.11" and python_version >= "3.9"
+executing==2.0.1 ; python_version >= "3.9"
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+gensim==4.3.2 ; python_version >= "3.9"
+importlib-metadata==7.0.0 ; python_version < "3.10" and python_version >= "3.9"
+importlib-resources==6.1.1 ; python_version < "3.10" and python_version >= "3.9"
+ipykernel==6.27.1 ; python_version >= "3.9"
+ipython==8.18.1 ; python_version >= "3.9"
+jedi==0.19.1 ; python_version >= "3.9"
+joblib==1.3.2 ; python_version >= "3.9"
+jupyter-client==8.6.0 ; python_version >= "3.9"
+jupyter-core==5.5.1 ; python_version >= "3.9"
+karateclub==1.2.3 ; python_version >= "3.9"
+kiwisolver==1.4.5 ; python_version >= "3.9"
+levenshtein==0.23.0 ; python_version >= "3.9"
+matplotlib-inline==0.1.6 ; python_version >= "3.9"
+matplotlib==3.8.2 ; python_version >= "3.9"
+nest-asyncio==1.5.8 ; python_version >= "3.9"
+networkx==3.2.1 ; python_version >= "3.9"
+node2vec==0.4.3 ; python_version >= "3.9"
+numpy==1.26.2 ; python_version >= "3.9"
+packaging==23.2 ; python_version >= "3.9"
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+parso==0.8.3 ; python_version >= "3.9"
+pexpect==4.9.0 ; sys_platform != "win32" and python_version >= "3.9"
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+platformdirs==4.1.0 ; python_version >= "3.9"
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+ptyprocess==0.7.0 ; sys_platform != "win32" and python_version >= "3.9"
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+pycparser==2.21 ; python_version >= "3.9" and implementation_name == "pypy"
+pygments==2.17.2 ; python_version >= "3.9"
+pygsp==0.5.1 ; python_version >= "3.9"
+pyparsing==3.1.1 ; python_version >= "3.9"
+python-dateutil==2.8.2 ; python_version >= "3.9"
+python-levenshtein==0.23.0 ; python_version >= "3.9"
+python-louvain==0.16 ; python_version >= "3.9"
+pytz==2023.3.post1 ; python_version >= "3.9"
+pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.9"
+pyzmq==25.1.2 ; python_version >= "3.9"
+rapidfuzz==3.5.2 ; python_version >= "3.9"
+scikit-learn==1.3.2 ; python_version >= "3.9"
+scipy==1.11.4 ; python_version >= "3.9"
+six==1.16.0 ; python_version >= "3.9"
+smart-open==5.1.0 ; python_version >= "3.9"
+stack-data==0.6.3 ; python_version >= "3.9"
+threadpoolctl==3.2.0 ; python_version >= "3.9"
+tornado==6.4 ; python_version >= "3.9"
+tqdm==4.66.1 ; python_version >= "3.9"
+traitlets==5.14.0 ; python_version >= "3.9"
+typing-extensions==4.9.0 ; python_version < "3.10" and python_version >= "3.9"
+wcwidth==0.2.12 ; python_version >= "3.9"
+zipp==3.17.0 ; python_version < "3.10" and python_version >= "3.9"
diff --git a/Chapter03/01_ImageClassification_TensorFlow.ipynb b/Chapter03/01_ImageClassification_TensorFlow.ipynb
new file mode 100644
index 0000000..09ce577
--- /dev/null
+++ b/Chapter03/01_ImageClassification_TensorFlow.ipynb
@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8c4e9e09-b0c6-4671-86a0-ca6bc73056b6",
+   "metadata": {},
+   "source": [
+    "# Image Classification with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "012237ce-0396-4c88-8b13-994c7a830421",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.datasets import fashion_mnist\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6080305f-eaac-49d2-a4cb-658a907186ac",
+   "metadata": {},
+   "source": [
+    "### Load and re-scale input data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1135c9e8-1765-48cc-beb8-25fd6ff363d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee2989f4-9b32-48f6-b669-8255aa9e9c79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train.astype('float32') / 255.\n",
+    "x_test = x_test.astype('float32') / 255.\n",
+    "\n",
+    "print (x_train.shape)\n",
+    "print (x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0525097f-4b57-4e9c-b850-966540589a30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes = {\n",
+    "    0: \"T-shirt\",\n",
+    "    1: \"Trouser\",\n",
+    "    2: \"Pullover\",\n",
+    "    3: \"Dress\",\n",
+    "    4: \"Coat\",\n",
+    "    5: \"Sandal\",\n",
+    "    6: \"Shirt\",\n",
+    "    7: \"Sneaker\",\n",
+    "    8: \"Bag\",\n",
+    "    9: \"Ankle boot\", \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0e2bc95-ee33-4024-99d2-4ba7cb4fd0c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 6\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(n):\n",
+    "    # display original\n",
+    "    ax = plt.subplot(1, n, i + 1)\n",
+    "    plt.imshow(x_test[i])\n",
+    "    plt.title(classes[y_test[i]])\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ace701-0cd8-4173-982c-8682a860dd26",
+   "metadata": {},
+   "source": [
+    "### Build model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2d549c8-a410-4caa-95a4-b0bb20a05236",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = tf.keras.models.Sequential([\n",
+    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
+    "  tf.keras.layers.Dense(128, activation='relu'),\n",
+    "  tf.keras.layers.Dropout(0.2),\n",
+    "  tf.keras.layers.Dense(10)\n",
+    "])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20ef704-3435-4f12-b41b-db42fcfb3b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af979aa-9ccb-4b92-b0fd-ef39fcf6f317",
+   "metadata": {},
+   "source": [
+    "### Train the network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53f02c48-3d8f-4e41-a9d4-703016a0dc19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
+    "optimizer = tf.keras.optimizers.Adam()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6427e500-41d2-43f1-b235-622d1d59572e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=optimizer,\n",
+    "              loss=loss_fn,\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef1fea96-97cc-4c84-bb0c-78417a571575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(\n",
+    "    x_train, \n",
+    "    y_train, \n",
+    "    validation_data=(x_test, y_test), \n",
+    "    epochs=20, \n",
+    "    batch_size=128,\n",
+    "    shuffle=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8c64c99-cbab-4503-8a1d-84f80c6f2af7",
+   "metadata": {},
+   "source": [
+    "### Classification beyond fully connected layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4649c36-1157-438b-bff0-01e980aa7da3",
+   "metadata": {},
+   "source": [
+    "For a slightly more complex and deeper network, try to train the model below that uses Convolution Neural Network (CNNs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "915392b5-805c-4536-89ac-2e5fb930dfdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_img = tf.keras.layers.Input(shape=(28, 28, 1))\n",
+    "\n",
+    "x = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Flatten(input_shape=(26, 26))(x)\n",
+    "x = tf.keras.layers.Dense(128, activation='relu')(x)\n",
+    "x = tf.keras.layers.Dropout(0.2)(x)\n",
+    "x = tf.keras.layers.Dense(10)(x)\n",
+    "\n",
+    "model = tf.keras.Model(input_img, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1c72892-976c-4763-9c90-ed5472438394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36ffd4c2-72b7-4fb3-83a3-0e2f87237e9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=optimizer,\n",
+    "              loss=loss_fn,\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93bdddf0-dcbb-4f58-9805-112af7f3bb69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(\n",
+    "    x_train, \n",
+    "    y_train, \n",
+    "    validation_data=(x_test, y_test), \n",
+    "    epochs=20, \n",
+    "    batch_size=128,\n",
+    "    shuffle=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99111fb3-8f59-45e0-8c61-cb24c99b7778",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap3",
+   "language": "python",
+   "name": "chap3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter03/01_Shallow_Embeddings.ipynb b/Chapter03/01_Shallow_Embeddings.ipynb
deleted file mode 100644
index c48db2c..0000000
--- a/Chapter03/01_Shallow_Embeddings.ipynb
+++ /dev/null
@@ -1,358 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def draw_graph(G, node_names={}, node_size=500):\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=True, node_size=node_size, edge_color='gray', arrowsize=30)\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    #nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Graph Factorization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "\n",
-    "G = nx.barbell_graph(m1=3, m2=2)\n",
-    "draw_graph(G)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(\"gem/intermediate\").mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gem.embedding.gf import GraphFactorization\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "draw_graph(G)\n",
-    "\n",
-    "gf = GraphFactorization(d=2,  data_set=None,max_iter=10000, eta=1*10**-4, regu=1.0)\n",
-    "gf.learn_embedding(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for x in G.nodes():\n",
-    "    \n",
-    "    v = gf.get_embedding()[x]\n",
-    "    ax.scatter(v[0],v[1], s=1000)\n",
-    "    ax.annotate(str(x), (v[0],v[1]), fontsize=12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## GraphRep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "from karateclub.node_embedding.neighbourhood.grarep import GraRep\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "draw_graph(G)\n",
-    "\n",
-    "gr = GraRep(dimensions=2,order=3)\n",
-    "gr.fit(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "ida = 4\n",
-    "idb = 5\n",
-    "for x in G.nodes():\n",
-    "    \n",
-    "    v = gr.get_embedding()[x]\n",
-    "    ax.scatter(v[ida],v[idb], s=1000)\n",
-    "    ax.annotate(str(x), (v[ida],v[idb]), fontsize=12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## HOPE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "from gem.embedding.hope import HOPE\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "draw_graph(G)\n",
-    "\n",
-    "hp = HOPE(d=4, beta=0.01)\n",
-    "hp.learn_embedding(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for x in G.nodes():\n",
-    "    \n",
-    "    v = hp.get_embedding()[x,2:]\n",
-    "    ax.scatter(v[0],v[1], s=1000)\n",
-    "    ax.annotate(str(x), (v[0],v[1]), fontsize=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## DeepWalk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "from karateclub.node_embedding.neighbourhood.deepwalk import DeepWalk\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "draw_graph(G)\n",
-    "\n",
-    "dw = DeepWalk(dimensions=2)\n",
-    "dw.fit(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for x in G.nodes():\n",
-    "    \n",
-    "    v = dw.get_embedding()[x]\n",
-    "    ax.scatter(v[0],v[1], s=1000)\n",
-    "    ax.annotate(str(x), (v[0],v[1]), fontsize=12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Node2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx\n",
-    "from node2vec import Node2Vec\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "draw_graph(G)\n",
-    "\n",
-    "node2vec = Node2Vec(G, dimensions=2)\n",
-    "model = node2vec.fit(window=10)\n",
-    "embeddings = model.wv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for x in G.nodes():\n",
-    "    \n",
-    "    v = model.wv[str(x)]\n",
-    "    ax.scatter(v[0],v[1], s=1000)\n",
-    "    ax.annotate(str(x), (v[0],v[1]), fontsize=16)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Edge2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from node2vec.edges import HadamardEmbedder\n",
-    "edges_embs = HadamardEmbedder(keyed_vectors=model.wv)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for x in G.edges():\n",
-    "    \n",
-    "    v = edges_embs[(str(x[0]), str(x[1]))]\n",
-    "    ax.scatter(v[0],v[1], s=1000)\n",
-    "    ax.annotate(str(x), (v[0],v[1]), fontsize=16)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Graph2Vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "import matplotlib.pyplot as plt\n",
-    "from karateclub import Graph2Vec\n",
-    "\n",
-    "n_graphs = 20\n",
-    "\n",
-    "def generate_radom():\n",
-    "    n = random.randint(6, 20)\n",
-    "    k = random.randint(5, n)\n",
-    "    p = random.uniform(0, 1)\n",
-    "    return nx.watts_strogatz_graph(n,k,p), [n,k,p]\n",
-    "\n",
-    "Gs = [generate_radom() for x in range(n_graphs)]\n",
-    "\n",
-    "model = Graph2Vec(dimensions=2, wl_iterations=10)\n",
-    "model.fit([x[0] for x in Gs])\n",
-    "embeddings = model.get_embedding()\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10,10))\n",
-    "\n",
-    "for i,vec in enumerate(embeddings):\n",
-    "    \n",
-    "    ax.scatter(vec[0],vec[1], s=1000)\n",
-    "    ax.annotate(str(i), (vec[0],vec[1]), fontsize=40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter03/02_ImageClassification_Pytorch.ipynb b/Chapter03/02_ImageClassification_Pytorch.ipynb
new file mode 100644
index 0000000..9606fde
--- /dev/null
+++ b/Chapter03/02_ImageClassification_Pytorch.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cfe91160-02ad-48cc-810b-1031f21397ff",
+   "metadata": {},
+   "source": [
+    "# Image Classification with PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60ea01e6-2184-4d88-a9e3-c05f70953f0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torchvision import datasets, transforms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83163df1-5732-459b-948a-b88d07d692cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4067288c-39ac-4f51-a5b3-f3a043f0c3a9",
+   "metadata": {},
+   "source": [
+    "### Load and re-scale input data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55daa661-47bb-4499-b8a4-dce7f5cadc22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer=transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cf93aa8-eaa0-4143-84fc-2c617d402bd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = datasets.FashionMNIST('./data', train=True, download=True, transform=transformer)\n",
+    "test_dataset = datasets.FashionMNIST('./data', train=False, transform=transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa2574d6-659d-4ef5-92a5-ffbe7036dc5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)\n",
+    "testloader = torch.utils.data.DataLoader(test_dataset, batch_size=test_dataset.data.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be30236f-5707-4517-9b6e-3eeb0601500f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes = {v: k for k, v in train_dataset.class_to_idx.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7b7f58e-291e-4084-933e-4e3357fe42fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 6\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(n):\n",
+    "    # display original\n",
+    "    ax = plt.subplot(1, n, i + 1)\n",
+    "    plt.imshow(test_dataset[i][0][0])\n",
+    "    plt.title(classes[test_dataset[i][1]])\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef148e96-02d9-45d2-825d-01951a45f047",
+   "metadata": {},
+   "source": [
+    "### Build model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60570c8d-b61d-4558-8d3d-e831279e898f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cbed0a8-9d1c-49b1-9e41-59d15b2e134d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Model(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.flatten = nn.Flatten()\n",
+    "        self.fc1 = nn.Linear(28*28, 128)\n",
+    "        self.dropout = nn.Dropout(0.2)\n",
+    "        self.fc2 = nn.Linear(128,10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.flatten(x)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.fc2(x)\n",
+    "        return F.log_softmax(x, dim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fa2d6f1-9db7-43d7-b00c-b44e5eba8a55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bc8880f-47a3-4b0f-8fee-be0f4b8a0b85",
+   "metadata": {},
+   "source": [
+    "### Train the network "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0c04fcb-4769-4022-ab84-89ee2412febc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.optim as optim\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "679b9446-9820-416a-8bd2-96b8712c33b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchmetrics.classification import MulticlassAccuracy\n",
+    "\n",
+    "accuracy = MulticlassAccuracy(num_classes=len(train_dataset.classes))\n",
+    "\n",
+    "for epoch in range(20):  # loop over the dataset multiple times\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    for i, data in enumerate(trainloader, 0):\n",
+    "        # get the inputs; data is a list of [inputs, labels]\n",
+    "        inputs, labels = data\n",
+    "\n",
+    "        # zero the parameter gradients\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        # forward + backward + optimize\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # print statistics\n",
+    "        running_loss += loss.item()\n",
+    "        if i % 200 == 199:    # print every 2000 mini-batches\n",
+    "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')                        \n",
+    "            running_loss = 0.0\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    for inputs, labels in testloader:\n",
+    "        preds = model(inputs)\n",
+    "        print(f\"Accuracy on validation set: {float(accuracy(preds, labels))}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15e22f40-9f03-4b89-8233-5306d7a4061b",
+   "metadata": {},
+   "source": [
+    "### Classification beyond fully connected layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8519786-c1bc-4bd3-b3dd-09fca96928d8",
+   "metadata": {},
+   "source": [
+    "For a slightly more complex and deeper network, try to train the model below that uses Convolution Neural Network (CNNs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ab7e5b5-ceac-4d3b-bd61-1c3274d090d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNNModel(nn.Module): \n",
+    "    def __init__(self): \n",
+    "        super(CNNModel, self).__init__() \n",
+    "        # 1 input channel (grayscale), 32 output filters \n",
+    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3) \n",
+    "        self.pool = nn.MaxPool2d(2, 2)  # Pooling with a 2x2 window \n",
+    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3) \n",
+    "        self.fc1 = nn.Linear(64 * 5 * 5, 64) \n",
+    "        # 64 filters output, 5x5 feature map  \n",
+    " \n",
+    "        self.fc2 = nn.Linear(64, 10)  # 10 output classes \n",
+    "\n",
+    "    def forward(self, x): \n",
+    "        x = self.pool(F.relu(self.conv1(x)))   \n",
+    "        x = self.pool(F.relu(self.conv2(x))) \n",
+    "        x = x.view(-1, 64 * 5 * 5) \n",
+    "        x = F.relu(self.fc1(x)) \n",
+    "        x = self.fc2(x) \n",
+    "        return F.log_softmax(x, dim=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a110e26e-73a2-42ef-8146-880e05656778",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = CNNModel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44762dd1-fcbf-4e9a-bfdf-f55d284bf78c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.optim as optim\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd76b705-2b7f-45c4-99c8-32984d239af9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchmetrics.classification import MulticlassAccuracy\n",
+    "\n",
+    "accuracy = MulticlassAccuracy(num_classes=len(train_dataset.classes))\n",
+    "\n",
+    "for epoch in range(20):  # loop over the dataset multiple times\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    for i, data in enumerate(trainloader, 0):\n",
+    "        # get the inputs; data is a list of [inputs, labels]\n",
+    "        inputs, labels = data\n",
+    "\n",
+    "        # zero the parameter gradients\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        # forward + backward + optimize\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # print statistics\n",
+    "        running_loss += loss.item()\n",
+    "        if i % 200 == 199:    # print every 2000 mini-batches\n",
+    "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')                        \n",
+    "            running_loss = 0.0\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    for inputs, labels in testloader:\n",
+    "        preds = model(inputs)\n",
+    "        print(f\"Accuracy on validation set: {float(accuracy(preds, labels))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "246b2cef-831a-4efe-83b3-f3125a9cd439",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap3",
+   "language": "python",
+   "name": "chap3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter03/03_Autoencoders.ipynb b/Chapter03/03_Autoencoders.ipynb
new file mode 100644
index 0000000..fef7b60
--- /dev/null
+++ b/Chapter03/03_Autoencoders.ipynb
@@ -0,0 +1,627 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoEncoder "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following we will show you how to create, train and use a simple autoencoder. We will then show you how to make an auto-encoder more robust against noise. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.datasets import fashion_mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train.astype('float32') / 255.\n",
+    "x_test = x_test.astype('float32') / 255.\n",
+    "\n",
+    "print (x_train.shape)\n",
+    "print (x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes = {\n",
+    "    0:\"T-shirt/top\",\n",
+    "    1: \"Trouser\",\n",
+    "    2: \"Pullover\",\n",
+    "    3: \"Dress\",\n",
+    "    4: \"Coat\",\n",
+    "    5: \"Sandal\",\n",
+    "    6: \"Shirt\",\n",
+    "    7: \"Sneaker\",\n",
+    "    8: \"Bag\",\n",
+    "    9: \"Ankle boot\", \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 6\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(n):\n",
+    "    # display original\n",
+    "    ax = plt.subplot(1, n, i + 1)\n",
+    "    plt.imshow(x_test[i])\n",
+    "    plt.title(classes[y_test[i]])\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "plt.show()\n",
+    "# plt.savefig(\"TrainingSet.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Autoencoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Flatten, Conv2D, Dropout, MaxPooling2D, UpSampling2D, Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_img = Input(shape=(28, 28, 1))\n",
+    "\n",
+    "x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "\n",
+    "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
+    "\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "x = Conv2D(16, (3, 3), activation='relu')(x)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "autoencoder = Model(input_img, decoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Model(input_img, encoded).summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.callbacks import TensorBoard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.fit(x_train, x_train,\n",
+    "                epochs=50,\n",
+    "                batch_size=128,\n",
+    "                shuffle=True,\n",
+    "                validation_data=(x_test, x_test),\n",
+    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.save(\"./data/Batch50.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder_first = load_model(\"./data/Batch50.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoded_imgs = autoencoder_first.predict(x_test)\n",
+    "\n",
+    "n = 6\n",
+    "plt.figure(figsize=(20, 7))\n",
+    "for i in range(1, n + 1):\n",
+    "    # Display original\n",
+    "    ax = plt.subplot(2, n, i)\n",
+    "    plt.imshow(x_test[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "    # Display reconstruction\n",
+    "    ax = plt.subplot(2, n, i + n)\n",
+    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.optimizers import Adam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.compile(optimizer=Adam(learning_rate=0.0005), loss='binary_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.fit(x_train, x_train,\n",
+    "                epochs=50,\n",
+    "                batch_size=128,\n",
+    "                shuffle=True,\n",
+    "                validation_data=(x_test, x_test),\n",
+    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.save(\"./data/Batch100.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoded_imgs = autoencoder.predict(x_test)\n",
+    "\n",
+    "n = 10\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(1, n + 1):\n",
+    "    # Display original\n",
+    "    ax = plt.subplot(2, n, i)\n",
+    "    plt.imshow(x_test[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "    # Display reconstruction\n",
+    "    ax = plt.subplot(2, n, i + n)\n",
+    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the trained layers in order to get the core representation in the middle layer of the autoencoder, and we represent them with the TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = Model(input_img, Flatten()(encoded)).predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsne = TSNE(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emb2d = tsne.fit_transform(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x,y = np.squeeze(emb2d[:, 0]), np.squeeze(emb2d[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.cm import tab10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary =  pd.DataFrame({\"x\": x, \"y\": y, \"target\": y_test, \"size\": 10})\n",
+    "\n",
+    "plt.figure(figsize=(10,8))\n",
+    "\n",
+    "for key, sel in summary.groupby(\"target\"):\n",
+    "    plt.scatter(sel[\"x\"], sel[\"y\"], s=10, color=tab10.colors[key], label=classes[key])\n",
+    "    \n",
+    "plt.legend()\n",
+    "plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Denoising"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Introducing noise in order to train more robust auto-encoders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import GaussianNoise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_img = Input(shape=(28, 28, 1))\n",
+    "\n",
+    "noisy_input = GaussianNoise(0.1)(input_img)\n",
+    "\n",
+    "x = Conv2D(16, (3, 3), activation='relu', padding='same')(noisy_input)\n",
+    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
+    "\n",
+    "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
+    "\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "x = Conv2D(16, (3, 3), activation='relu')(x)\n",
+    "x = UpSampling2D((2, 2))(x)\n",
+    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "noisy_autoencoder = Model(input_img, decoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noisy_autoencoder.compile(optimizer='adam', loss='binary_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noisy_autoencoder.fit(x_train, x_train,\n",
+    "                epochs=50,\n",
+    "                batch_size=128,\n",
+    "                shuffle=True,\n",
+    "                validation_data=(x_test, x_test),\n",
+    "                callbacks=[TensorBoard(log_dir='/tmp/noisy_autoencoder')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.save(\"./data/DenoisingAutoencoder.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise_factor = 0.1\n",
+    "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
+    "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
+    "\n",
+    "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
+    "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
+    "\n",
+    "decoded_imgs_denoised = noisy_autoencoder.predict(x_test_noisy)\n",
+    "\n",
+    "n = 6\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "for i in range(1, n + 1):\n",
+    "    # Display original\n",
+    "    ax = plt.subplot(3, n, i)\n",
+    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    if i==0:\n",
+    "        plt.ylabel(\"Original\")\n",
+    "    else:\n",
+    "        ax.get_yaxis().set_visible(False)\n",
+    "        \n",
+    "    # Display reconstruction\n",
+    "    ax = plt.subplot(3, n, i + n)\n",
+    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    if i==0:\n",
+    "        plt.ylabel(\"Vanilla Autoencoder\")\n",
+    "    else:\n",
+    "        ax.get_yaxis().set_visible(False)\n",
+    "     \n",
+    "    ax = plt.subplot(3, n, i + 2*n)\n",
+    "    plt.imshow(decoded_imgs_denoised[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    if i==0:\n",
+    "        plt.ylabel(\"Denoising Autoencoder\")\n",
+    "    else:\n",
+    "        ax.get_yaxis().set_visible(False)\n",
+    "    \n",
+    "        \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoded_imgs = noisy_autoencoder.predict(x_test_noisy)\n",
+    "\n",
+    "n = 10\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(1, n + 1):\n",
+    "    # Display original\n",
+    "    ax = plt.subplot(2, n, i)\n",
+    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "    # Display reconstruction\n",
+    "    ax = plt.subplot(2, n, i + n)\n",
+    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],  
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap3",
+   "language": "python",
+   "name": "chap3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter03/03_Structural_deep_neural_embeddings.ipynb b/Chapter03/03_Structural_deep_neural_embeddings.ipynb
deleted file mode 100644
index 5a2c321..0000000
--- a/Chapter03/03_Structural_deep_neural_embeddings.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Structural Deep Network Embedding"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "import tensorflow as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gem.embedding.sdne import SDNE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graph = nx.karate_club_graph()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m1 = SDNE(d=2, beta=5, alpha=1e-5, nu1=1e-6, nu2=1e-6, K=3,n_units=[50, 15,], rho=0.3, n_iter=10, \n",
-    "          xeta=0.01,n_batch=50,\n",
-    "          modelfile=['enc_model.json', 'dec_model.json'],\n",
-    "          weightfile=['enc_weights.hdf5', 'dec_weights.hdf5'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m1 = SDNE(d=2, beta=5, alpha=1e-5, nu1=1e-6, nu2=1e-6, K=3,n_units=[50, 15,], rho=0.3, n_iter=50, \n",
-    "          xeta=0.01,n_batch=100,\n",
-    "          modelfile=['enc_model.json', 'dec_model.json'],\n",
-    "          weightfile=['enc_weights.hdf5', 'dec_weights.hdf5'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m1.learn_embedding(graph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = list(zip(*m1.get_embedding()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(x, y, 'o',linewidth=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter03/04_GraphAutoEncoder_PyGeometric.ipynb b/Chapter03/04_GraphAutoEncoder_PyGeometric.ipynb
new file mode 100644
index 0000000..6610add
--- /dev/null
+++ b/Chapter03/04_GraphAutoEncoder_PyGeometric.ipynb
@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f5d647f0-6d7b-4e4b-b5cd-c674dba76a22",
+   "metadata": {},
+   "source": [
+    "# Graph Auto Encoder with PyG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9403ccd6-3353-4edd-9ba2-beaab617afa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "import torch_geometric.transforms as T\n",
+    "from torch_geometric.datasets import Planetoid\n",
+    "\n",
+    "from torch_geometric.nn import GAE, GCNConv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c80d137d-313f-4b8b-8473-c787a59c97ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2694e0b8-7ad2-4ab0-bd66-ec9e9615c9cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATASET_NAME=\"Cora\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "409d5939-8d16-4db9-a17f-c5cab6a2d4aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = T.Compose([\n",
+    "    T.NormalizeFeatures(),\n",
+    "    T.RandomLinkSplit(num_val=0., num_test=0.1, is_undirected=True,\n",
+    "                      split_labels=True, add_negative_train_samples=False),\n",
+    "])\n",
+    "# path = os.path.join(\"/home/deusebio/Personal/graph_machine_learning/data\", 'data')\n",
+    "path = os.path.join(os.getcwd(), 'data')\n",
+    "dataset = Planetoid(path, DATASET_NAME, transform=transform)\n",
+    "train_data, val_data, test_data = dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fe46bed-054c-4f52-a4da-ed3728a3f41c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Train edges (positive): {train_data.pos_edge_label_index.shape[1]}\")\n",
+    "print(f\"Test edges (positive): {test_data.pos_edge_label_index.shape[1]}\")\n",
+    "print(f\"Test edges (negative): {test_data.neg_edge_label_index.shape[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dedf968d-6e87-451a-aaa2-972ce21f4aca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GCNEncoder(torch.nn.Module):\n",
+    "    def __init__(self, num_node_features, num_embedding):\n",
+    "        super().__init__()\n",
+    "        self.conv1 = GCNConv(num_node_features, 2 * num_embedding)\n",
+    "        self.conv2 = GCNConv(2 * num_embedding, num_embedding)\n",
+    "\n",
+    "    def forward(self, x, edge_index):\n",
+    "        x = self.conv1(x, edge_index).relu()\n",
+    "        return self.conv2(x, edge_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40de5839-fdf8-4935-b2d8-f46adb5ab4eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_features = dataset.num_features\n",
+    "n_embeddings = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9240419-00b2-4f01-885a-169814989d21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = GAE(GCNEncoder(n_features, n_embeddings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baa87287-e068-463f-9cb5-be032a2273ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = model.to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da9b26c1-c070-4e98-a0a1-26783b0ed7d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for epoch in range(20):  # loop over the dataset multiple times\n",
+    "\n",
+    "    model.train()\n",
+    "\n",
+    "    # zero the parameter gradients\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    z = model.encode(train_data.x, train_data.edge_index)\n",
+    "    loss = model.recon_loss(z, train_data.pos_edge_label_index)\n",
+    "\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    \n",
+    "    # Test/Evaluate\n",
+    "    model.eval()\n",
+    "    z = model.encode(test_data.x, test_data.edge_index)\n",
+    "    auc, ap = model.test(z, test_data.pos_edge_label_index, test_data.neg_edge_label_index)\n",
+    "    \n",
+    "    print(f\"Performance on validation set => AUC: {auc} AP: {ap}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43533588-03f7-45c9-8d97-1e618148a9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap3",
+   "language": "python",
+   "name": "chap3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter03/04_Graph_Neural_Network.ipynb b/Chapter03/04_Graph_Neural_Network.ipynb
deleted file mode 100644
index b3b7d37..0000000
--- a/Chapter03/04_Graph_Neural_Network.ipynb
+++ /dev/null
@@ -1,577 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "NBVKcDWHeGoR"
-   },
-   "source": [
-    "# Unsupervised graph representation learning using Graph ConvNet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lb6FvAQ3eUNs"
-   },
-   "source": [
-    "In this notebook we will be performing unsupervised graph representation learning using Graph ConvNet as encoder.\n",
-    "\n",
-    "The model embeds a graph by using stacked Graph ConvNet layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "RyweACZPHYQA"
-   },
-   "outputs": [],
-   "source": [
-    "#from networkx import karate_club_graph, to_numpy_matrix\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "from scipy.linalg import sqrtm\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "G = nx.barbell_graph(m1=10, m2=4)\n",
-    "\n",
-    "order = np.arange(G.number_of_nodes())\n",
-    "A = nx.to_numpy_matrix(G, nodelist=order)\n",
-    "I = np.eye(G.number_of_nodes())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "JgSsTLzr9a4y"
-   },
-   "outputs": [],
-   "source": [
-    "np.random.seed(7)\n",
-    "\n",
-    "A_hat = A + np.eye(G.number_of_nodes()) # add self-connections\n",
-    "\n",
-    "D_hat = np.array(np.sum(A_hat, axis=0))[0]\n",
-    "D_hat = np.array(np.diag(D_hat))\n",
-    "D_hat = np.linalg.inv(sqrtm(D_hat))\n",
-    "\n",
-    "A_hat = D_hat @ A_hat @ D_hat\n",
-    "\n",
-    "def glorot_init(nin, nout):\n",
-    "  sd = np.sqrt(6.0 / (nin + nout))\n",
-    "  return np.random.uniform(-sd, sd, size=(nin, nout))\n",
-    "\n",
-    "class GCNLayer():\n",
-    "  def __init__(self, n_inputs, n_outputs):\n",
-    "      self.n_inputs = n_inputs\n",
-    "      self.n_outputs = n_outputs\n",
-    "      self.W = glorot_init(self.n_outputs, self.n_inputs)\n",
-    "      self.activation = np.tanh\n",
-    "      \n",
-    "  def forward(self, A, X):\n",
-    "      self._X = (A @ X).T # (N,N)*(N,n_outputs) ==> (n_outputs,N)\n",
-    "      H = self.W @ self._X # (N, D)*(D, n_outputs) => (N, n_outputs)\n",
-    "      H = self.activation(H)\n",
-    "      return H.T # (n_outputs, N)\n",
-    "\n",
-    "gcn1 = GCNLayer(G.number_of_nodes(), 8)\n",
-    "gcn2 = GCNLayer(8, 4)\n",
-    "gcn3 = GCNLayer(4, 2)\n",
-    "\n",
-    "H1 = gcn1.forward(A_hat, I)\n",
-    "H2 = gcn2.forward(A_hat, H1)\n",
-    "H3 = gcn3.forward(A_hat, H2)\n",
-    "\n",
-    "embeddings = H3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 319
-    },
-    "id": "OhVzlenz1x97",
-    "outputId": "a6659970-a1e6-4b7a-a6a6-e0903ceefe5f"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def draw_graph(G, filename=None, node_size=50):\n",
-    "  pos_nodes = nx.spring_layout(G)\n",
-    "  nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')\n",
-    "  \n",
-    "  pos_attrs = {}\n",
-    "  for node, coords in pos_nodes.items():\n",
-    "    pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "\n",
-    "  plt.axis('off')\n",
-    "  axis = plt.gca()\n",
-    "  axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "  axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "\n",
-    "embeddings = np.array(embeddings)\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 265
-    },
-    "id": "XLgjmzRLLLcs",
-    "outputId": "d056de50-0e08-49f8-ea79-fc0c4bc52778"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(embeddings[:, 0], embeddings[:, 1])\n",
-    "plt.savefig('embedding_gcn.png',dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "C83YCCDLG-Cv"
-   },
-   "source": [
-    "## Unsupervised GCN training using similarity graph distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lpDNVIR8fMJT"
-   },
-   "source": [
-    "For the next example, we need to install StellarGraph, the python library we will be using to build the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "7BtFQ8YoL4xz",
-    "outputId": "df0e9283-5201-4237-960c-9ef5391bb7b1"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zsh:1: no matches found: stellargraph[demos]==1.2.1\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "# install StellarGraph\n",
-    "!pip install -q stellargraph[demos]==1.2.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "iafwVXyrL6q6"
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import os\n",
-    "\n",
-    "import stellargraph as sg\n",
-    "from stellargraph.mapper import FullBatchNodeGenerator\n",
-    "from stellargraph.layer import GCN\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
-    "from sklearn import preprocessing, model_selection\n",
-    "from IPython.display import display, HTML\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "VHU1UGiHfw1e"
-   },
-   "source": [
-    "In this demo, we will be using the PROTEINS dataset, already integrated in StellarGraph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 68
-    },
-    "id": "zhttMYjFMu5f",
-    "outputId": "15cf0fdd-8eec-41eb-b307-3e26575b692a"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "Each graph represents a protein and graph labels represent whether they are are enzymes or non-enzymes. The dataset includes 1113 graphs with 39 nodes and 73 edges on average for each graph. Graph nodes have 4 attributes (including a one-hot encoding of their label), and each graph is labelled as belonging to 1 of 2 classes."
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dataset = sg.datasets.PROTEINS()\n",
-    "display(HTML(dataset.description))\n",
-    "graphs, graph_labels = dataset.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 315
-    },
-    "id": "n1A345-rMx8V",
-    "outputId": "3d31a583-a43d-4478-bddc-c4da1c109228"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 42, Edges: 162\n",
-      "\n",
-      " Node types:\n",
-      "  default: [42]\n",
-      "    Features: float32 vector, length 4\n",
-      "    Edge types: default-default->default\n",
-      "\n",
-      " Edge types:\n",
-      "    default-default->default: [162]\n",
-      "        Weights: all 1 (default)\n",
-      "        Features: none\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>450</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   label\n",
-       "1    663\n",
-       "2    450"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# let's print some info to better understand the dataset\n",
-    "print(graphs[0].info())\n",
-    "graph_labels.value_counts().to_frame()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "tVx9OQoSgViY"
-   },
-   "source": [
-    "### Model definition\n",
-    "It's now time to build-up the model. StellarGraph offers several utility function to load and process the dataset, as well as define the GNN model and train."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "gn1egwLSgUd3"
-   },
-   "outputs": [],
-   "source": [
-    "# TODO\n",
-    "generator = sg.mapper.PaddedGraphGenerator(graphs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "id": "vBJo0MkBNCLE"
-   },
-   "outputs": [],
-   "source": [
-    "# define a GCN model containing 2 layers of size 64 and 32, respectively. \n",
-    "# ReLU activation function is used to add non-linearity between layers\n",
-    "gc_model = sg.layer.GCNSupervisedGraphClassification(\n",
-    "    [64, 32], [\"relu\", \"relu\"], generator, pool_all_layers=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "id": "6WYIXEO1NHdW"
-   },
-   "outputs": [],
-   "source": [
-    "inp1, out1 = gc_model.in_out_tensors()\n",
-    "inp2, out2 = gc_model.in_out_tensors()\n",
-    "\n",
-    "vec_distance = tf.norm(out1 - out2, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "id": "dG5WFf7LNWTL"
-   },
-   "outputs": [],
-   "source": [
-    "pair_model = Model(inp1 + inp2, vec_distance)\n",
-    "embedding_model = Model(inp1, out1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "id": "liCd_C-JKebp"
-   },
-   "outputs": [],
-   "source": [
-    "def graph_distance(graph1, graph2):\n",
-    "    spec1 = nx.laplacian_spectrum(graph1.to_networkx(feature_attr=None))\n",
-    "    spec2 = nx.laplacian_spectrum(graph2.to_networkx(feature_attr=None))\n",
-    "    k = min(len(spec1), len(spec2))\n",
-    "    return np.linalg.norm(spec1[:k] - spec2[:k])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "wN0RSDgSKtVM"
-   },
-   "outputs": [],
-   "source": [
-    "graph_idx = np.random.RandomState(0).randint(len(graphs), size=(100, 2))\n",
-    "targets = [graph_distance(graphs[left], graphs[right]) for left, right in graph_idx]\n",
-    "train_gen = generator.flow(graph_idx, batch_size=10, targets=targets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "id": "HQpoEAdvKzWL"
-   },
-   "outputs": [],
-   "source": [
-    "pair_model.compile(optimizers.Adam(1e-2), loss=\"mse\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 297
-    },
-    "id": "aYL3qZXYLGrX",
-    "outputId": "fd0867e2-6eae-47d4-80b7-07ad5d9485e3"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "history = pair_model.fit(train_gen, epochs=500, verbose=0)\n",
-    "sg.utils.plot_history(history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oArvDvO3LOXc"
-   },
-   "outputs": [],
-   "source": [
-    "embeddings = embedding_model.predict(generator.flow(graphs))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jDEfCnALMFm2"
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.manifold import TSNE\n",
-    "\n",
-    "tsne = TSNE(2)\n",
-    "two_d = tsne.fit_transform(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 267
-    },
-    "id": "6XUWp7ZzMMtC",
-    "outputId": "2f2702c9-ab2e-424a-e6eb-d72c45eef4f5"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light",
-      "tags": []
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(two_d[:, 0], two_d[:, 1], c=graph_labels.cat.codes, cmap=\"jet\", alpha=0.4)\n",
-    "plt.savefig('embedding_TSNE.png',dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "i34QgSA_P_sM"
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "Unsupervised_GraphML.ipynb",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/Chapter03/05_GraphAutoEncoder_StellarGraph.ipynb b/Chapter03/05_GraphAutoEncoder_StellarGraph.ipynb
new file mode 100644
index 0000000..e080818
--- /dev/null
+++ b/Chapter03/05_GraphAutoEncoder_StellarGraph.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "393d1f8c-162d-43c6-9d3a-3e795bf6467a",
+   "metadata": {},
+   "source": [
+    "# Graph AutoEncoder with StellarGraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65a6d0fb-bb0f-4af0-8ba9-6a0c902cec49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stellargraph.data import EdgeSplitter\n",
+    "from stellargraph.mapper import FullBatchLinkGenerator\n",
+    "from stellargraph.layer import GCN, LinkEmbedding\n",
+    "\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from stellargraph import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d27bd90-c522-48e2-9929-7417b3ce904b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = datasets.Cora()\n",
+    "G, _ = dataset.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e59277b-12b7-4e8f-9cdc-eaf4c96d1771",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(G.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ab4de94-e416-49b2-8e02-5217d5f410e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_splitter_test = EdgeSplitter(G)\n",
+    "\n",
+    "G_test, edge_ids_test, edge_labels_test = edge_splitter_test.train_test_split(\n",
+    "    p=0.1, method=\"global\", keep_connected=True\n",
+    ")\n",
+    "\n",
+    "edge_splitter_train = EdgeSplitter(G_test)\n",
+    "\n",
+    "G_train, edge_ids_train, edge_labels_train = edge_splitter_train.train_test_split(\n",
+    "    p=0.1, method=\"global\", keep_connected=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "261e8cdd-455d-4607-a95c-c26ec6aaf109",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_gen = FullBatchLinkGenerator(G, method=\"gcn\")\n",
+    "train_flow = train_gen.flow(edge_ids_train, edge_labels_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5820186d-1728-494b-9e2c-6a906d7ff3f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_gen = FullBatchLinkGenerator(G, method=\"gcn\")\n",
+    "test_flow = train_gen.flow(edge_ids_test, edge_labels_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7897bc20-a2eb-4887-8bde-3ca5756cd62d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gcn = GCN(\n",
+    "    layer_sizes=[16, 16], activations=[\"relu\", \"relu\"], generator=train_gen, dropout=0.3\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c27562a4-9f93-4c23-87b1-ad3a0f46c19a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp, x_out = gcn.in_out_tensors()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be2d54a1-9561-410a-9c8f-54b805450dc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = LinkEmbedding(activation=\"relu\", method=\"ip\")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38cd0093-ef71-4110-8f59-43e510b86edc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = keras.layers.Reshape((-1,))(prediction)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33ccc882-df0c-40ad-a401-725b3a64aac3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(lr=0.01),\n",
+    "    loss=keras.losses.binary_crossentropy,\n",
+    "    metrics=[\"binary_accuracy\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1dbc5d61-4e11-4ec8-bb06-8b206beb698d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5760e12-f39a-4ce8-8c9d-9ba8ae61d4ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history = model.fit(train_flow, validation_data=test_flow, epochs=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d6f0caf-529a-4609-ade3-61715426e4b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stellargraph.utils import plot_history\n",
+    "\n",
+    "plot_history(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e2130e3-6710-4d60-afa2-c0c7254921a5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap3",
+   "language": "python",
+   "name": "chap3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter03/poetry.lock b/Chapter03/poetry.lock
new file mode 100644
index 0000000..d4e663c
--- /dev/null
+++ b/Chapter03/poetry.lock
@@ -0,0 +1,3256 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
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+
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+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "96067e895d3606373b258e77ec97a4a18d7269ec92ebd9bc00c244ff19239192"
diff --git a/Chapter03/pyproject.toml b/Chapter03/pyproject.toml
new file mode 100644
index 0000000..c0bdb7b
--- /dev/null
+++ b/Chapter03/pyproject.toml
@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 3"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+tensorflow = "^2.6.0" 
+tensorflow-io-gcs-filesystem = "==0.21.0" # This needs pinning - see https://stackoverflow.com/a/76477590 
+stellargraph= "^1.2.1"
+protobuf= "^3.20"
+torch = "^2.1.0"
+chardet = "==5.2.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Chapter03/requirements.txt b/Chapter03/requirements.txt
new file mode 100644
index 0000000..15027ec
--- /dev/null
+++ b/Chapter03/requirements.txt
@@ -0,0 +1,125 @@
+absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+aiohappyeyeballs==2.4.3 ; python_version >= "3.8" and python_version < "3.9"
+aiohttp==3.10.8 ; python_version >= "3.8" and python_version < "3.9"
+aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9"
+async-timeout==4.0.3 ; python_version >= "3.8" and python_version < "3.9"
+attrs==24.2.0 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+cachetools==5.5.0 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+chardet==5.2.0 ; python_version >= "3.8" and python_version < "3.9"
+charset-normalizer==3.3.2 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.6 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+filelock==3.16.1 ; python_version >= "3.8" and python_version < "3.9"
+flatbuffers==2.0.7 ; python_version >= "3.8" and python_version < "3.9"
+frozenlist==1.4.1 ; python_version >= "3.8" and python_version < "3.9"
+fsspec==2024.9.0 ; python_version >= "3.8" and python_version < "3.9"
+gast==0.4.0 ; python_version >= "3.8" and python_version < "3.9"
+gensim==4.3.3 ; python_version >= "3.8" and python_version < "3.9"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.9"
+google-auth==2.35.0 ; python_version >= "3.8" and python_version < "3.9"
+google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+grpcio==1.66.2 ; python_version >= "3.8" and python_version < "3.9"
+h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.10 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.1 ; python_version >= "3.8" and python_version < "3.9"
+jinja2==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9"
+keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9"
+keras==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9"
+libclang==18.1.1 ; python_version >= "3.8" and python_version < "3.9"
+lightning-utilities==0.11.7 ; python_version >= "3.8" and python_version < "3.9"
+markdown==3.7 ; python_version >= "3.8" and python_version < "3.9"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9"
+multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9"
+networkx==3.1 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.24.4 ; python_version >= "3.8" and python_version < "3.9"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+packaging==24.1 ; python_version >= "3.8" and python_version < "3.9"
+pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9"
+protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9"
+psutil==6.0.0 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9"
+requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9"
+rsa==4.9 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==1.3.2 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+setuptools==75.1.0 ; python_version >= "3.8" and python_version < "3.9"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==7.0.4 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9"
+sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9"
+termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9"
+torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9"
+torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9"
+torchmetrics==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.66.5 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9"
+tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9"
+werkzeug==3.0.4 ; python_version >= "3.8" and python_version < "3.9"
+wheel==0.44.0 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+yarl==1.13.1 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter04/01_Shallow_Embeddings.ipynb b/Chapter04/01_Shallow_Embeddings.ipynb
new file mode 100644
index 0000000..f81d013
--- /dev/null
+++ b/Chapter04/01_Shallow_Embeddings.ipynb
@@ -0,0 +1,651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4 - Shallow Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import draw_graph, FIGURES_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graph Factorization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "\n",
+    "G = nx.barbell_graph(m1=3, m2=2)\n",
+    "draw_graph(G, node_size=200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "(Path(\"gem\") / \"intermediate\").mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "G = nx.barbell_graph(m1=10, m2=4)\n",
+    "\n",
+    "def get_color(x: int):\n",
+    "    if x<10:\n",
+    "        return \"lightblue\", \"steelblue\"\n",
+    "    elif x<14:\n",
+    "        return \"lightcoral\", \"red\"\n",
+    "    else:\n",
+    "        return \"lightgreen\", \"limegreen\"\n",
+    "\n",
+    "color_code = lambda x: get_color(x) # for x in G.nodes()\n",
+    "\n",
+    "edge_color_code = lambda x, y: (color_code(x)[0], color_code(y)[1])\n",
+    "\n",
+    "def plot_embeddings(V):\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(5,5))\n",
+    "    \n",
+    "    for x in range(V.shape[0]):\n",
+    "\n",
+    "        color = color_code(x)\n",
+    "        ax.scatter(V[x, 0],V[x, 1], s=1)\n",
+    "        ax.text(V[x, 0], V[x, 1], str(x),\n",
+    "                ha=\"center\", va=\"center\", \n",
+    "                bbox=dict(boxstyle=\"circle,pad=0.3\",\n",
+    "                          fc=color[0], ec=color[1], lw=2))\n",
+    "    return fig, ax\n",
+    "\n",
+    "def plot_edge_embeddings(V, edges):\n",
+    "    fig, ax = plt.subplots(figsize=(5,5))\n",
+    "    \n",
+    "    for x in range(V.shape[0]):\n",
+    "        source, target = edges[x]  \n",
+    "        color = edge_color_code(source, target)\n",
+    "        ax.scatter(V[x, 0],V[x, 1], s=1)\n",
+    "        ax.text(V[x, 0], V[x, 1], str(x),\n",
+    "                ha=\"center\", va=\"center\", \n",
+    "                bbox=dict(boxstyle=\"circle,pad=0.3\",\n",
+    "                          fc=color[0], ec=color[1], lw=2))\n",
+    "    return fig, ax\n",
+    "    \n",
+    "def draw_graph(G, node_names={}, filename=None, node_size=50, layout = None, plot_weight=False):\n",
+    "    pos_nodes = nx.spring_layout(G) if layout is None else layout(G)\n",
+    "    node_names = {k: k for k, v in G.nodes.items()} if not node_names else node_names\n",
+    "    node_colors = [color_code(x)[0] for x in G.nodes()]\n",
+    "    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray', node_color=node_colors)\n",
+    "\n",
+    "    pos_attrs = {}\n",
+    "    for node, coords in pos_nodes.items():\n",
+    "        pos_attrs[node] = (coords[0], coords[1])\n",
+    "\n",
+    "    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif', font_size=15)\n",
+    "\n",
+    "    if plot_weight:\n",
+    "        edge_labels=dict([((a,b,),d[\"weight\"]) for a,b,d in G.edges(data=True)])\n",
+    "        nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels)\n",
+    "\n",
+    "    plt.axis('off')\n",
+    "    axis = plt.gca()\n",
+    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
+    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
+    "\n",
+    "    if filename:\n",
+    "        plt.savefig(FIGURES_DIR / filename, format=\"png\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "draw_graph(G, node_size=400, filename= \"barbell.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./gf not found. Reverting to Python implementation. Please compile gf, place node2vec in the path and grant executable permission\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2.95850890e-03,  1.53587266e-05],\n",
+       "       [ 2.95633484e-03,  1.65974931e-05],\n",
+       "       [ 2.96335880e-03,  1.86992083e-05],\n",
+       "       [ 2.96369483e-03, -1.15516559e-05],\n",
+       "       [ 2.99888702e-03, -3.13947500e-06],\n",
+       "       [ 2.83273191e-03,  4.45982842e-05],\n",
+       "       [ 3.20303266e-03, -1.02110718e-05],\n",
+       "       [ 2.34517829e-03, -2.48212379e-04],\n",
+       "       [ 7.51771759e-04,  2.20302979e-03],\n",
+       "       [ 8.08401156e-03,  7.73875139e-04],\n",
+       "       [ 6.82307744e-03,  1.21123721e-03],\n",
+       "       [ 7.74028818e-03,  3.57769367e-03],\n",
+       "       [ 8.92395437e-03,  5.95435897e-03],\n",
+       "       [-2.24327522e-03,  6.34222630e-03],\n",
+       "       [-8.50338290e-03,  7.76064514e-04],\n",
+       "       [-8.50440132e-03,  7.74746187e-04],\n",
+       "       [-8.50836606e-03,  7.83177466e-04],\n",
+       "       [-8.48239084e-03,  7.61556817e-04],\n",
+       "       [-8.53850058e-03,  7.64439345e-04],\n",
+       "       [-8.45672279e-03,  8.42846869e-04],\n",
+       "       [-8.26616668e-03,  1.18857751e-03],\n",
+       "       [-9.16950282e-03, -1.30328692e-03],\n",
+       "       [-8.02977677e-03,  1.70413675e-03],\n",
+       "       [-8.65218628e-03,  2.57665364e-03]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from gem.embedding.gf import GraphFactorization\n",
+    "\n",
+    "gf = GraphFactorization(d=2,  data_set=None,max_iter=10000, eta=1*10**-4, regu=1.0)\n",
+    "gf.learn_embedding(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 500x500 with 1 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x73d59f500250>)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "V=np.matrix(gf.get_embedding())\n",
+    "\n",
+    "plot_embeddings(V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GraphRep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "from karateclub.node_embedding.neighbourhood.grarep import GraRep\n",
+    "\n",
+    "gr = GraRep(dimensions=2,order=3)\n",
+    "gr.fit(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k=3\n",
+    "ida = (k-1)*2\n",
+    "idb = (k-1)*2+1\n",
+    "\n",
+    "V = np.matrix(gr.get_embedding())[:, [ida,idb]]\n",
+    "\n",
+    "fig, ax = plot_embeddings(V)\n",
+    "ax.set_title(f\"K={k}\")\n",
+    "\n",
+    "plt.savefig(FIGURES_DIR / f\"K{k}.png\", format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HOPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVD error (low rank): 0.052092\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 500x500 with 1 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x73d5718dd160>)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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bH8/Dkya1W/X3b73Fn7Zu5bfXXMNr3/0u4cHBTH7mGepbTRDQeNFF7X5Xl3z+OeTnA9A8YABNZ5zR5badERPDwxMnsuXOO1l/660k2WxMfuYZDrZ65MTo398zkhY8Z5cvv9y99omcBApLkZPhrbcAMIKDcSUn++y6atQo7s/M5BvtjGQ1DIM/vv0291x+OZOSkznHbmfZt75FWXU1/ywu9pZz2+00H51q7sMPPS987qotW7wfXWPGeCY86ELbAK4fM4avjRzJsOhoUuLi+Hl2NlUNDWx3OHzKuc47r2XlyM9C5FSmsBT5qh04AHv2ANA8aFC3njf84tAhHDU1jB8xwrstKjSU1CFDeKf1s4xA0+DBng+GAf/9b9fbV1jo/djc6vJpdzU2NfGXwkIiQ0I4Jz6+/bYd830ipyrdsxT5qrU6A2zu5kTjjpoaAOIiIny2x/Xrx4Ej+45ytz52URF0dTRrUdFxtw9gfUkJeS++SK3Lhb1/f16++WZi+vXzLRQWhjsqCqvT6fN9IqcqnVmKfNXq6rwfDT8ObDHCwlpWujPXbKuyx9O+y4YP54077uDVvDwyzziD6atX8+UxQe7Tvu7OgytyEigsRb5qrS+7tp7XtQvij5xRHnsWeeDw4TZnmxyd/g6698aPE2gfQL/gYEbExHBRYiKPXnstgVYrz7R3Gfho+/Q2EjkNKCxFvmpxcd6P1oqKblUdOmAA8RERbGo1j2xVfT2Fe/cy9pj7i9ZDh9r9TlOt7i92t33tcRsGDcfOJOR2Y62s7H7bRE4S3bMU+aqlpHieK6yvJ6C0tM3umoYGdrUKqS8qK/lw/34GhIWRaLNx57hx/GbzZkZGRzN0wAB+vnEj9v79mXTMqFqfY194Ydfbd+GFnonOgYD9+3EPGtSltkWHh/PI5s1MOOss4vv3p6K2lifeeYf9VVVcd/RRkSOsBw9iOfqoS3faJnKSKCxFvmqBgXD++fD22wQcOoS1vNzn7SL/LS3lG3/5i3f9vn//G4Bp553HH7/1Lb5/ySUcbmzkB2vW4KyvZ1xSEn+78UZCW1/ObGwk8IsvPJ+jomDkyK63Ly3N+zHok09wtQqzztq2+Jpr+PjgQZ774APKa2uJDgvjgsGD+dett5JyzNlj4JH5cAFITe1620ROEr11RG8dkZOh1WuvGjIyqM/O7tHDBxUWEr5mjWdlxgzPGz66qrERkpLA4cCwWKieO/fE5qw9lttNxKOPEnD0DLW4GM46q+eOL9INeuuIyKns1lshJASA4Pfew3I887d2xOUipPWD/t2d6i44GGbOBMBiGIS88UbPtQ0I+uijlqDMylJQymnBb2FZUVFBbm4ukZGR2Gw28vLyqGln+Hhr9fX1zJo1i5iYGCIiIsjJycFxzMwfd911F6mpqYSEhHD++ef7q/ki/hUTA7fcAoCloYGwf/zDM3lADwjduLEljC677Pguc95+Oxx5tCNk2zYCWg0oOhGW6mpC//Wvlg1z5/bIcUX8zW9hmZuby/bt28nPz2ft2rVs3ryZmUd+W+3I3LlzWbNmDatXr2bTpk2UlpYyefLkNuVuvfVWpk6d6q+mi3w1fvlLODJ4JmjnTkI2bz7hQwZu307wkbeFEBLieXnz8Rg8GBYt8q6Gv/QSltaja4+Hy0X46tVYjz5nOnUqTJx4YscU+Yr45Z5lUVERo0ePZtu2baQdGSywfv16Jk6cyN69e0lISGhTx+l0Ehsby6pVq5gyZQoAxcXFpKSkUFBQwLhx43zK/+xnP+Pll1/m/fff73b7dM9SThlr18I3vuFdrb/8chq+9jWf+Vi7Kuj99wl75RUsR/9KP/ww3H338bfN7YYrroAjIe6OiuJwbi7u43jUw1JbS/jzz7cMOoqLg+3b4ei7N0VOkpN6z7KgoACbzeYNSoCsrCysVitbt25tt05hYSEul4usrCzvtuTkZJKSkig4+pvycWpoaKCqqspnETklXHONJ9SOCN28mX7Ll2MtL+/yISyHDxP2wguEv/xyS1Dm5cH8+SfWNqsVXnwRjjySYnU6ifjTnwh+661uTVYQWFxMxGOPtQRl//6wbp2CUk4rfnl0pKysjLhjh4oHBhIdHU1ZWVmHdYKDg7Ed8wLc+Pj4Dut01aJFi3jggQdO6BgifnP33RAQAPPmARC4ezcRjz2GKyWFxtRUmpOS2k623txMQFkZQe+9R/CHH7Y8swhw553whz/4vPbruMXGet69efXV8MEHWJqbCcvPJ2TbNhpTU2k891yMqKi231VXR1BxMcHbthHY+nnPmBj417/0uIicdroVlgsWLOBXv/pVp2WKTsFJkRcuXMi8I/8Qgee0OzEx8SS2SOQYc+d6AmTGDNi1C4vbTfD27QRv345hteKOi8MdGQkWC5bDhwkoK8Ny7Kw4MTHw2GPw7W/3TFAeZbdDQQHcfz8sXgyGgbWyktANGwjdsAF3v3644+IwQkKgqYmAgwdbZudp7RvfgD/9yXufVuR00q2wnD9/PtOnT++0zIgRI7Db7Rw4cMBne1NTExUVFdg7eIuB3W6nsbGRyspKn7NLh8PRYZ2uCgkJIeTIMH2RU9bll3vePfmrX3lC5cjfIYvbTUBZGQEdXWGJiICbboKf/tRnqroeFRYGjzwCU6bAQw/B+vXe0bvWw4exdjZa9vzzPc+U3nBDz4a4yFeoW2EZGxtLbGysabmMjAwqKyspLCwk9cjllo0bN+J2u0lPT2+3TmpqKkFBQWzYsIGcnBwASkpK2L17NxkZGd1ppsjpq18/ePBBuO8+eOklzyXLwkLPg/ut7xOOGOE5E73iCsjNha9qoFpGhud+46efwl/+Am+/7Wlf6zlkw8M9AZmWBtOmQXq6QlJOe36bwWfChAk4HA6WLVuGy+VixowZpKWlserInJP79u0jMzOTFStWMHbsWADuvPNO1q1bx/Lly4mMjGTOnDkAbGn15vadO3dSU1PDsmXLeP3113n++ecBGD16NMHBwV1qm0bDymmnrg6qqz2BGRHhWU4VhgGVlVBf75nQICqqWy+0FjmZupoHfvsTvXLlSmbPnk1mZiZWq5WcnByWLFni3e9yuSgpKaG21bvsFi9e7C3b0NBAdnY2S5cu9Tnud7/7XTZt2uRdv+CCCwD47LPPGDZsmL+6I3JyhYV5Jwk45VgsMGDAyW6FiF9pblidWYqI9FmaG1ZERKSHKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERMKCxFRERM+DUsKyoqyM3NJTIyEpvNRl5eHjU1NZ3Wqa+vZ9asWcTExBAREUFOTg4Oh8O7/4MPPmDatGkkJiYSFhZGSkoKv//97/3ZDRER6eP8Gpa5ubls376d/Px81q5dy+bNm5k5c2andebOncuaNWtYvXo1mzZtorS0lMmTJ3v3FxYWEhcXx7PPPsv27du57777WLhwIY8++qg/uyIiIn2YxTAMwx8HLioqYvTo0Wzbto20tDQA1q9fz8SJE9m7dy8JCQlt6jidTmJjY1m1ahVTpkwBoLi4mJSUFAoKChg3bly73zVr1iyKiorYuHFjl9pWVVVFVFQUTqeTyMjI4+yhiIic7rqaB347sywoKMBms3mDEiArKwur1crWrVvbrVNYWIjL5SIrK8u7LTk5maSkJAoKCjr8LqfTSXR0dIf7GxoaqKqq8llERES6ym9hWVZWRlxcnM+2wMBAoqOjKSsr67BOcHAwNpvNZ3t8fHyHdbZs2cLzzz/f6eXdRYsWERUV5V0SExO72RsREenLuh2WCxYswGKxdLoUFxf7o61tfPTRR1x77bX89Kc/5etf/3qH5RYuXIjT6fQue/bs+UraJyIivUNgdyvMnz+f6dOnd1pmxIgR2O12Dhw44LO9qamJiooK7HZ7u/XsdjuNjY1UVlb6nF06HI42dXbs2EFmZiYzZ87k/vvv77Q9ISEhhISEdFpGRESkI90Oy9jYWGJjY03LZWRkUFlZSWFhIampqQBs3LgRt9tNenp6u3VSU1MJCgpiw4YN5OTkAFBSUsLu3bvJyMjwltu+fTtXXnklt9xyCz//+c+72wUREZFu8dtoWIAJEybgcDhYtmwZLpeLGTNmkJaWxqpVqwDYt28fmZmZrFixgrFjxwJw5513sm7dOpYvX05kZCRz5swBPPcmwXPp9corryQ7O5uHH37Y+10BAQFdCnHQaFgREfHoah50+8yyO1auXMns2bPJzMzEarWSk5PDkiVLvPtdLhclJSXU1tZ6ty1evNhbtqGhgezsbJYuXerd/+KLL/Lll1/y7LPP8uyzz3q3Dx06lM8//9yf3RERkT7Kr2eWpyqdWYqICJwCz1mKiIj0FgpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLEREREwpLERERE34Ny4qKCnJzc4mMjMRms5GXl0dNTU2nderr65k1axYxMTFERESQk5ODw+Hw7i8vL+fqq68mISGBkJAQEhMTmT17NlVVVf7sioiI9GF+Dcvc3Fy2b99Ofn4+a9euZfPmzcycObPTOnPnzmXNmjWsXr2aTZs2UVpayuTJk1sabLVy7bXX8o9//IOPP/6Y5cuX89prr3HHHXf4sysiItKHWQzDMPxx4KKiIkaPHs22bdtIS0sDYP369UycOJG9e/eSkJDQpo7T6SQ2NpZVq1YxZcoUAIqLi0lJSaGgoIBx48a1+11Llizh4YcfZs+ePV1qW1VVFVFRUTidTiIjI4+zhyIicrrrah747cyyoKAAm83mDUqArKwsrFYrW7dubbdOYWEhLpeLrKws77bk5GSSkpIoKChot05paSkvvfQS48eP77AtDQ0NVFVV+SwiIiJd5bewLCsrIy4uzmdbYGAg0dHRlJWVdVgnODgYm83msz0+Pr5NnWnTphEeHs7gwYOJjIzkySef7LAtixYtIioqyrskJiYeZ69ERKQv6nZYLliwAIvF0ulSXFzsj7b6WLx4Me+99x6vvPIKn376KfPmzeuw7MKFC3E6nd6lq5drRUREAAK7W2H+/PlMnz690zIjRozAbrdz4MABn+1NTU1UVFRgt9vbrWe322lsbKSystLn7NLhcLSpY7fbsdvtJCcnEx0dzWWXXcaPf/xjBg0a1Oa4ISEhhISEdLGHIiIivrodlrGxscTGxpqWy8jIoLKyksLCQlJTUwHYuHEjbreb9PT0duukpqYSFBTEhg0byMnJAaCkpITdu3eTkZHR4Xe53W7Ac29SRESkp/ltNCzAhAkTcDgcLFu2DJfLxYwZM0hLS2PVqlUA7Nu3j8zMTFasWMHYsWMBuPPOO1m3bh3Lly8nMjKSOXPmALBlyxYA1q1bh8Ph4KKLLiIiIoLt27dzzz33EB0dzZtvvtmldmk0rIiIQNfzoNtnlt2xcuVKZs+eTWZmJlarlZycHJYsWeLd73K5KCkpoba21rtt8eLF3rINDQ1kZ2ezdOlS7/6wsDCeeOIJ5s6dS0NDA4mJiUyePJkFCxb4sysiItKH+fXM8lSlM0sREYFT4DlLERGR3kJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKRnsLM4AACAASURBVCIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYkJhKSIiYsKvYVlRUUFubi6RkZHYbDby8vKoqanptE59fT2zZs0iJiaGiIgIcnJycDgc7ZYtLy9nyJAhWCwWKisr/dEFERER/4Zlbm4u27dvJz8/n7Vr17J582ZmzpzZaZ25c+eyZs0aVq9ezaZNmygtLWXy5Mntls3Ly2PMmDH+aLqIiEgLw0927NhhAMa2bdu82/71r38ZFovF2LdvX7t1KisrjaCgIGP16tXebUVFRQZgFBQU+JRdunSpMX78eGPDhg0GYBw6dKjLbXM6nQZgOJ3ObvZKRER6k67mgd/OLAsKCrDZbKSlpXm3ZWVlYbVa2bp1a7t1CgsLcblcZGVlebclJyeTlJREQUGBd9uOHTt48MEHWbFiBVareRcaGhqoqqryWURERLrKb2FZVlZGXFycz7bAwECio6MpKyvrsE5wcDA2m81ne3x8vLdOQ0MD06ZN4+GHHyYpKalLbVm0aBFRUVHeJTEx8Th6JCIifVW3w3LBggVYLJZOl+LiYn+0FYCFCxeSkpLCjTfe2K06TqfTu+zZs8dv7RMRkd4nsLsV5s+fz/Tp0zstM2LECOx2OwcOHPDZ3tTUREVFBXa7vd16drudxsZGKisrfc4uHQ6Ht87GjRv53//+x4svvgiAYRgADBw4kPvuu48HHnigzXFDQkIICQnpch9FRERa63ZYxsbGEhsba1ouIyODyspKCgsLSU1NBTxB53a7SU9Pb7dOamoqQUFBbNiwgZycHABKSkrYvXs3GRkZAPztb3+jrq7OW2fbtm3ceuutvPHGG4wcObK73RERETHV7bDsqpSUFK6++mpuu+02li1bhsvlYvbs2dxwww0kJCQAsG/fPjIzM1mxYgVjx44lKiqKvLw85s2bR3R0NJGRkcyZM4eMjAzGjRsH0CYQDx486P2+Y+91ioiI9AS/hSXAypUrmT17NpmZmVitVnJycliyZIl3v8vloqSkhNraWu+2xYsXe8s2NDSQnZ3N0qVL/dlMERGRTlmMozf9+pCqqiqioqJwOp1ERkae7OaIiMhJ0tU80NywIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJhSWIiIiJvwalhUVFeTm5hIZGYnNZiMvL4+amppO69TX1zNr1ixiYmKIiIggJycHh8PhU8ZisbRZ/vrXv/qzKyIi0of5NSxzc3PZvn07+fn5rF27ls2bNzNz5sxO68ydO5c1a9awevVqNm3aRGlpKZMnT25T7s9//jP79+/3Ltddd52/uiEiIn2cxTAMwx8HLioqYvTo0Wzbto20tDQA1q9fz8SJE9m7dy8JCQlt6jidTmJjY1m1ahVTpkwBoLi4mJSUFAoKChg3bpyn0RYLf//73487IKuqqoiKisLpdBIZGXmcPRQRkdNdV/PAb2eWBQUF2Gw2b1ACZGVlYbVa2bp1a7t1CgsLcblcZGVlebclJyeTlJREQUGBT9lZs2YxcOBAxo4dy9NPP01nmd/Q0EBVVZXPIiIi0lWB/jpwWVkZcXFxvl8WGEh0dDRlZWUd1gkODsZms/lsj4+P96nz4IMPcuWVVxIeHs6rr77K9773PWpqarjrrrvaPe6iRYt44IEHTrBHIiLSV3X7zHLBggXtDrBpvRQXF/ujrV4//vGPueSSS7jgggu49957+eEPf8jDDz/cYfmFCxfidDq9y549e/zaPhER6V26fWY5f/58pk+f3mmZESNGYLfbOXDggM/2pqYmKioqsNvt7daz2+00NjZSWVnpc3bpcDg6rAOQnp7OQw89RENDAyEhIW32h4SEtLtdRESkK7odlrGxscTGxpqWy8jIoLKyksLCQlJTUwHYuHEjbreb9PT0duukpqYSFBTEhg0byMnJAaCkpITdu3eTkZHR4Xe9//77DBgwQIEoIiJ+4bd7likpKVx99dXcdtttLFu2DJfLxezZs7nhhhu8I2H37dtHZmYmK1asYOzYsURFRZGXl8e8efOIjo4mMjKSOXPmkJGR4R0Ju2bNGhwOB+PGjSM0NJT8/Hx+8YtfcPfdd/urKyIi0sf5LSwBVq5cyezZs8nMzMRqtZKTk8OSJUu8+10uFyUlJdTW1nq3LV682Fu2oaGB7Oxsli5d6t0fFBTEY489xty5czEMgzPOOIPf/va33Hbbbf7sioiI9GF+e87yVKbnLEVEBE6B5yxFRER6C4WliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICb+FZUVFBbm5uURGRmKz2cjLy6OmpqbTOvX19cyaNYuYmBgiIiLIycnB4XC0Kbd8+XLGjBlDaGgocXFxzJo1y1/dEBER8V9Y5ubmsn37dvLz81m7di2bN29m5syZndaZO3cua9asYfXq1WzatInS0lImT57sU+a3v/0t9913HwsWLGD79u289tprZGdn+6sbIiIiWAzDMHr6oEVFRYwePZpt27aRlpYGwPr165k4cSJ79+4lISGhTR2n00lsbCyrVq1iypQpABQXF5OSkkJBQQHjxo3j0KFDDB48mDVr1pCZmXnc7auqqiIqKgqn00lkZORxH0dERE5vXc0Dv5xZFhQUYLPZvEEJkJWVhdVqZevWre3WKSwsxOVykZWV5d2WnJxMUlISBQUFAOTn5+N2u9m3bx8pKSkMGTKEb3/72+zZs6fT9jQ0NFBVVeWziIiIdJVfwrKsrIy4uDifbYGBgURHR1NWVtZhneDgYGw2m8/2+Ph4b51du3bhdrv5xS9+we9+9ztefPFFKioquOqqq2hsbOywPYsWLSIqKsq7JCYmnmAPRUSkL+lWWC5YsACLxdLpUlxc7K+24na7cblcLFmyhOzsbMaNG8dzzz3HJ598wuuvv95hvYULF+J0Or2L2ZmoiIhIa4HdKTx//nymT5/eaZkRI0Zgt9s5cOCAz/ampiYqKiqw2+3t1rPb7TQ2NlJZWelzdulwOLx1Bg0aBMDo0aO9+2NjYxk4cCC7d+/usE0hISGEhIR02m4REZGOdCssY2NjiY2NNS2XkZFBZWUlhYWFpKamArBx40bcbjfp6ent1klNTSUoKIgNGzaQk5MDQElJCbt37yYjIwOASy65xLt9yJAhgOcRlYMHDzJ06NDudEVERKTL/DIaFmDChAk4HA6WLVuGy+VixowZpKWlsWrVKgD27dtHZmYmK1asYOzYsQDceeedrFu3juXLlxMZGcmcOXMA2LJli/e41113HTt37uTxxx8nMjKShQsXsmvXLt5//32CgoK61DaNhhURETjJo2EBVq5cSXJyMpmZmUycOJFLL72Uxx9/3Lvf5XJRUlJCbW2td9vixYu55ppryMnJ4fLLL8dut/PSSy/5HHfFihWkp6czadIkxo8fT1BQEOvXr+9yUIqIiHSX384sT2U6sxQRETgFzixFRER6C4WliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiICYWliIiIicCT3QAREZGOOBud3Pf5fWyr2UYTTT77AgkktV8qi4YvIio4yq/tUFiKiMgp5/F9j/Mnx586LdNEE1sPb+XKj64E4Nb4W5k1eJZf2mMxDMPwy5FPYVVVVURFReF0OomMjOx2/Wc2lfDcGztp7uAnlxTTjye+97UTa6SISB9UVlPGpI8nndAx/n7m30mKSOpS2a7mgcKyG2E564nN7Cyr7nL5AAv8bf6VhIWFHU8zRUT6lHs/uZfXql9rs90WZCM+LJ6Y4BjCAjz/ntY111HRWIGj3sGhxkNt6oyPGM9vz/yt6XcqLDvR3bDcV+Hk1sfePO7vuzzFzn1TUo+7vohIbzezeCaFtYU+25LCkzg76mwGhgzEYrG0W88wDA42HGSHcwdf1H7hs29M6Bj+PPrPnX6vwrIT3QnL//xvD4te/tBnW3BQAGcOiybRHkmMLZzQ4ACa3QaHqupxHKyh5PNyKpz1PnXiokJ45q6sHu+LiMjp7v8++z/+fujv3vVQayjpA9MZ2m9ot46z+/Bu3j74NvXuln9/J0ZN5KGRD3VYR2HZia7+cEr2VXDX0wU+25IGRdK/XwjO6npcTW6sVgsR4cEMHBBOQmwEAweEYxgGX+x38kbhHmrrXN66idHhPDnrCr/1S0TkdFPXWMelH13qXe8X0I+vD/o6/YP6H9fxql3V5JflU9NU49226ZxNRARHtFteYdmJrv5wsh/6p/ez1QJYLLjdnf+4YgeEc86oWEYNjabR1cy/39rF/i9b/qc9+O0LST9r0An3QUSkN7jovYtw4wYgyBLEpMGTiAzq/sDL1qpd1awtXYvL7TlZsWJl24Xb2i3b1TzQpAQdmP6HjT7rbgPToAT48lAtr7/zBWs3fUJDYzMTLhvJwAEtA3x+8sJ7Pd5WEZHT0cc1H3uDEiA1OvWEgxKgf1B/Loq+yLvuxs2O6h0ndEydWXbwm0Trs8qjWt+rHDggnNDgQNxH71WW11DyWTkHK+t8yk+4bCQhQQG8mF/sDdvrM4bz3azR/umciMhpIuO9DBppBGBgyEAmDJrQ4UCe7jIMg/X71/Nlw5cABBDAOxe+06bcKXFmWVFRQW5uLpGRkdhsNvLy8qipqem0Tn19PbNmzSImJoaIiAhycnJwOBze/cuXL8disbS7HDhwoEfa/dDqd33WLRYYc1YcN37jHC65IJGkQVGEhwZhtVoIDLQSGx3OOaPimHxVMhMvP4OI8GAAGl3NrNu8k6ZmN2efEes93uqCz3qknSIip7OjQQmQHJnsDcr8xfk8kvkI9ybdy/1n3s+TNz6J4xOHT93n5z7PQxc+xD0J93DfqPt4MvdJHB+3lLFYLKREpnjXm2nmRM4N/RqWubm5bN++nfz8fNauXcvmzZuZOXNmp3Xmzp3LmjVrWL16NZs2baK0tJTJkyd790+dOpX9+/f7LNnZ2YwfP564uLgeafebxS0/8KBAK5PGjyLjvCEEBQZ0Ws9isZBoj+T67BSGxHtuTrua3LxW8BlnDY3ukbaJiPQGpXWl3s9BliCfka+fvvUpl+Zdyg/+/QPufOlO3C43y3KW0XC4wVsm8bxEvvPod1jw9gLuePEODMPgjzl/xN3cclk3sV8iQdYg7/ry0uXH3V6/XYYtKipi9OjRbNu2jbS0NADWr1/PxIkT2bt3LwkJCW3qOJ1OYmNjWbVqFVOmTAGguLiYlJQUCgoKGDduXJs6X375JYMHD+app57ipptu6lLbOjvt/sxRxR2PvwF4ziivGT+KhLjuj8pqanKzZtMnHCg/DMA5Z8Sy11FFZbXnf/aSWzM4a7ACVET6pluKbuGjuo8AiA+NJ3tQdodlaw7WcP+Z9zNn7RxGXjyy3TKl20v59WW/5v7C+xk4fKB3+6v7X6WsvgzwDPR5+4K3CbC0nPic9MuwBQUF2Gw2b1ACZGVlYbVa2bp1a7t1CgsLcblcZGW1PI+YnJxMUlISBQUF7dZZsWIF4eHh3nBtT0NDA1VVVT5LR557c6f38/nJ8e0GZV1NDU//4ifcfuVFTDtvBD+64Rvs/N/7PmUCA61cOXYogQGeywo7Pv0SW2Sod/9vXvF9dlNEpC8pqSvxfo4O7vzEoa7KMxYk3Bbe7v6Gww1sXbmVmKEx2AbbfPa1PrYbN286j2+CGb+FZVlZWZvLooGBgURHR1NWVtZhneDgYGw2387Gx8d3WOepp57iO9/5TqdTyi1atIioqCjvkpiY2G65ipp63iz2fE9oSCAXjm7/EY+lP57PB1s2c9ev/sBv/7GB8y4ZzwMzplLu2O9TLqp/KOee6fkZuA2oq2+ZMb+ytgERkb6qmWbv52BrcIfl3G43f//R3xmePpxBx/yb/OZTb/LDxB9yb+K9FG0o4s6X7iQw2Pf9ICEBIT7rLx186bja2+2wXLBgQYcDbI4uxcXFx9WY7iooKKCoqIi8vLxOyy1cuBCn0+ld9uzZ0/7xShw0Hxmxmjw8hsCAtj+ehvo63n51HTfffT9nXzSOQUOHM3XO3diThvHv51a0KZ8yIpajg7sqq1tmlQgM6Pz+p4hIb2ahZdSrQcd3A1+850X2F+3nlidvabMv9fpU7vnPPcxZO4fYkbEsv3U5rnqXTxm34fZZ/+DwB8c10Kfbr+iaP38+06dP77TMiBEjsNvtbUanNjU1UVFRgd1ub7ee3W6nsbGRyspKn7NLh8PRbp0nn3yS888/n9TUzuddDQkJISQkpNMyAB/vd3o/D01o/91o7qZm3M3NBB1zvODQUIoL2w5L7t8vmOioMMor62hobPlN6pzEAabtERHprcKsYdS4PU9H1Ljaf0rixR++yI5/72DOP+e0ubwKEBYZRlhkGLEjYxmaNpQfjfgRH/7zQ1JzWjKh9Uw+ANXN1ext3EtiSPtXGDvS7bCMjY0lNjbWtFxGRgaVlZUUFhZ6w2zjxo243W7S09PbrZOamkpQUBAbNmwgJycHgJKSEnbv3k1GRoZP2ZqaGl544QUWLVrU3S506DNHyxtFYmztX9YNi4jgrPNTeXHp7xgyYhRRA2N5858v8/H7hdiThrVbZ+CAcMpbPX8JMG+SnrMUkb7rhoE38OSBJwEobyz32WcYBn+792/875//Y/Y/ZhMzNMb8gIanXlOD7wuiyxvK2xT9rO6zboel3+5ZpqSkcPXVV3Pbbbfxzjvv8NZbbzF79mxuuOEG70jYffv2kZyczDvveM7IoqKiyMvLY968ebz++usUFhYyY8YMMjIy2oyEff7552lqauLGG2/ssTbXNXp+yEGB1k4fE7nr13/AMAxuG38hN4wZxrpnnuLSSddhsbb/4wwLafs7iV7bJSJ92cxBLY8ROl1On7PLF+95kXdfeJebHr+JkIgQqhxVVDmqaKzzPJd58POD5C/OZ8/7ezi09xCfbf2MP8/4M0GhQYy+quVE5HDTYZyuliuGR9W569psM9PtM8vuWLlyJbNnzyYzMxOr1UpOTg5Llizx7ne5XJSUlFBbW+vdtnjxYm/ZhoYGsrOzWbp0aZtjP/XUU0yePLnNYKATYT1yc9FtGBiG0eFMEvakYTz07EvU19ZSV1PNgLh4Hpl7O/GJ7c+Q7z7m+nhokGYZFJG+LeCYcRsfV3/MhdEXAvDW028B8Og3HvUpM+3RaaR/J52gkCB2Fexi07JN1FXW0T+2PyMvHsn313+f/rH9fY7Z3v3Q1o+OdJVfwzI6OppVq1Z1uH/YsGFtbrSGhoby2GOP8dhjj3V67C1btvRIG1sbGBnK519W09xsUFPbSP9+nd/nDA0PJzQ8nBpnJe+/uYmb7r6/3XKVVb6v6/rz97/WU00WETltjQkbw4d1nsfoPq7+mJSoFMICwvhdxe86rRc1KIrbX7i90zL1zfV8XPWxdz2IIFx4Bv8MCOz+mBGd4rQyalDLoJ4DFbUdlvvvG//hv2+8jmPvbj54axM/vWUKg0ecwZWTp7YpaxgGXx5qOVZ4sJVoXYIVEeHPKS0vZm50N7L14NYTmpKuta3lW2lwtzyi1y+gn/fzmeFndvt4fj2zPN2c3WqE6seflzOygxGrtTVVrPztIsrL9hNhszHuqol8Z+4CAoOC2pTdU1bl83zl3++d0PMNFxE5TWX0y6DgsGfSmd21u9lRtYOzo84+oWMWOYv44vAX3vWBwQOpaKwAICkkif4B3Z+VTWHZyoUjYomLCuOAs47d+6sor6wlpp0ZIy6Z8E0umfBN0+MZhsEHreaZvSPrrB5tr4jI6e7Rsx4l9b2WRz0KKwppNpo5N+rcbr+BxDAMtju3894h31chJvZL5GDjQQCusF1xXO3UZdhWAqwWvpHWMkjnP9u+8E5ScDyKdpVTeuTFzzERIXwzvf05DUVE+rIVI30ndHn/0Pu8WvYq1a7qDmq0VeOqIb8sv01Qfsv+LYqrPBPlWLCQMzDnuNqosDzGdWOHkTQwAoCDh+rY/O4Xx3UNvfRANVveb5kpaN43zyPA2jPvaRMR6U3OjjqbC0Iv8NnmqHfwyt5X2HxgM2V1ZTQbzW3qNRvNOOocvHHgDV7e+7J3wvSjJtsn82HNh9Q1ex4VGR81nsEhg4+rjXr5czuzzJeUVjLvz1toOnJWOWxwFJenDW33ecljGYbBx59X8OZ7u2lq9tSfcEEiP7hmTM92QkSkF9nbsJcp26d4R6wey4oVW7CNsADPAMm65joqGytx4263/A2Db+CLhi8oOOi5H9rP2o8XRr+APdh3NriT/taR09lZCTYWfOsC73OXn+9zsnr9Dj765IDPlHWtGYZB6YFq1r3xKf/Z9oU3KMeeEcusCed8ZW0XETkdDQkZwtwhczvc78ZNRWMF++r2sa9uHxWNFe0G5ch+I7lp2E3sqt/lDUqAeUPmtQnK7tCZZSe/Sbz9sYPf/OMDqutaftMJDLASH9OPgQPCCA0Jwu12c6iqHkf5YaoPN/rU//p5Q7hr0rkEtTMhu4iI+DIMg1/u+SUvHnyx23XP738+YwaOobaplrfL32Zv7V7vvty4XOYOntvugKGu5oHCspMfDnhe2/Xouo94q8TRabnWBvYPZc7Ecxh3ZvyJNlVEpE9xG25+s/c3PP/l895tgZZAhoUNo7G5kXqjHgODqMAo0gamERwQjGEYVDRW8En1J+yq2UWT0fK43s1xN3PX4Ls6HFmrsOxEd8LyqM8PVLO28AveLCrj0OG276IMCrCSMsTGpAuHckmKXWeTIiLHyTAM8g/l88s9v8TZ7Du3a5AliOiQaJ97lxUNFbgM33ud0YHRLExayJW2Kzv9LoVlJ44nLFsrr67nswPV1DU0ERBgIT4qjKGx/dt9/6WIiByfclc5f9r/J/5Z8U/q3fXmFYBwazjXxFzDzEEzuzStncKyEycaliIi8tWpbq7mn+X/5J3qdyiqLeKAy/ddyfFB8aSEpzAuchwToyf6TG1nRmHZCYWliMjpq7KpkprmGixYiAiIICowyrxSB7qaB5ruTkRETiu2QBu2wJ57PWNX9MmwPHoyXVVVdZJbIiIiJ9PRHDC7yNonw7K62jPfYGJi4kluiYiInAqqq6uJiur4cm6fvGfpdrspLS2lf//+3Z7V/mSpqqoiMTGRPXv2nNb3WXtDP3pDH6B39KM39AHUj5PJMAyqq6tJSEjAau34iYY+eWZptVoZMmTIyW7GcYmMjDxt/hB2pjf0ozf0AXpHP3pDH0D9OFk6O6M8Sg8GioiImFBYioiImAj42c9+9rOT3QjpmoCAAL72ta8RGHh6Xz3vDf3oDX2A3tGP3tAHUD9OdX1ygI+IiEh36DKsiIiICYWliIiICYWliIiICYWliIiICYWliIiICYXlKaKiooLc3FwiIyOx2Wzk5eVRU1PTaZ36+npmzZpFTEwMERER5OTk4HA42pRbvnw5Y8aMITQ0lLi4OGbNmuWvbvi1HwDl5eUMGTIEi8VCZWWlP7oA+KcfH3zwAdOmTSMxMZGwsDBSUlL4/e9/32Ntfuyxxxg2bBihoaGkp6fzzjvvdFp+9erVJCcnExoayrnnnsu6det89huGwU9+8hMGDRpEWFgYWVlZfPLJJz3W3o70ZD9cLhf33nsv5557Lv369SMhIYGbb76Z0tJSf3ejx/9/tHbHHXdgsVj43e9+19PN9uGPPhQVFfHNb36TqKgo+vXrx0UXXcTu3bv91YWeY8gp4eqrrzbOO+884+233zbeeOMN44wzzjCmTZvWaZ077rjDSExMNDZs2GC8++67xrhx44yLL77Yp8wjjzxiJCQkGCtXrjR27txpfPDBB8Yrr7xy2vXjqGuvvdaYMGGCARiHDh3yRxcMw/BPP5566injrrvuMv7zn/8Yn376qfHMM88YYWFhxh/+8IcTbu9f//pXIzg42Hj66aeN7du3G7fddpths9kMh8PRbvm33nrLCAgIMH79618bO3bsMO6//34jKCjI+N///uct88tf/tKIiooyXn75ZeODDz4wvvnNbxrDhw836urqTri9X1U/KisrjaysLOP55583iouLjYKCAmPs2LFGamqq3/rgj3609tJLLxnnnXeekZCQYCxevPi06sPONbI9vAAABz5JREFUnTuN6Oho45577jHee+89Y+fOncYrr7zS4TFPJQrLU8COHTsMwNi2bZt327/+9S/DYrEY+/bta7dOZWWlERQUZKxevdq7raioyACMgoICwzAMo6KiwggLCzNee+01/3bgCH/146ilS5ca48ePNzZs2ODXsPR3P1r73ve+Z1xxxRUn3OaxY8cas2bN8q43NzcbCQkJxqJFi9ot/+1vf9uYNGmSz7b09HTj9ttvNwzDMNxut2G3242HH37Yu7+ystIICQkxnnvuuRNub0d6uh/teeeddwzA+OKLL3qm0e3wVz/27t1rDB482Pjoo4+MoUOH+jUs/dGHqVOnGjfeeKN/Guxnugx7CigoKMBms/1/e/cX0lQfxgH861wzImqNua2SBVHW3EUGoe2qC1fzYjTXhTBQ+nMxIiIIERcVoy6iyAvLiyIopJv+MMIuvLBgI/zXwEE0WRMj+4tzqDQKyrN2nvfiZef16F5XuKPufZ8PHMZ+53d2nq9ze1B/82Dfvn3SmN1uh0qlQjgcznlMJBJBOp2G3W6Xxnbv3g2z2YyhoSEAwPPnzyGKIr58+QKLxYKKigo0Njbi06dPRZUDAGKxGC5fvoz79+8vemWAQlAyx3ypVAo6nW5J9QqCgEgkIju3SqWC3W7/13MPDQ3J5gOAw+GQ5o+PjyORSMjmbNy4EbW1tYvmWQolcuSSSqVQUlICrVaZiwcrlUMURTQ3N6O1tRVWq1WR2rOUyCCKInp6elBZWQmHwwGDwYDa2lp0d3crF6SAuFmuAolEAgaDQTamVquh0+mQSCT+9RiNRrPgBW80GqVj3r17B1EUceXKFXR0dCAQCGBmZgYHDx6EIAhFk2N2dhYejwfXr1+H2WwueN25alIix3yDg4N49OgRvF7vkuqdmppCJpOB0Wj87XMnEolF52dv/+Qxl0qJHPP9/PkTbW1t8Hg8il0VQ6kc165dg1qtxpkzZwpf9DxKZEgmk/j+/TuuXr2K+vp6PHv2DG63G0eOHMGLFy+UCVJA3CwV5PP5UFJSsugWj8cVO78oikin07h58yYcDgf279+PBw8eYGxsDKFQqGhynDt3DhaLBU1NTUt6nJXOMdfIyAhcLhf8fj8OHTq0LOf8v0un02hsbAQR4datWytdzh+JRCK4ceMGurq6iuYavPOJoggAcLlcOHv2LKqrq+Hz+eB0OnH79u0Vri6//9Z/ul1lWlpacOzYsUXnbN++HSaTCclkUjb+69cvzMzMwGQy5TzOZDJBEAR8/fpV9tPM5OSkdMzmzZsBAFVVVdL+8vJy6PX6P1p9ttI5gsEgotEoAoEAgL9XaQKAXq/H+fPncenSpaLIkRWLxVBXVwev14sLFy78Vu2L0ev1KC0tXbCCONe559a72Pzs7eTkpPR9lL1fXV295JpzUSJHVrZRfvjwAcFgUNFrLSqRo6+vD8lkUvablUwmg5aWFnR0dOD9+/erPoNer4darZa9HwGAxWJBf39/AatXyEr/0ZT9s6BkeHhYGuvt7f2tBSWBQEAai8fjsgUlo6OjBEC2wGd6eppUKhX19vYWTY63b99SNBqVtnv37hEAGhwcVGQVnVI5iIhGRkbIYDBQa2trQWuuqamh06dPS/czmQxt3bp10cUYTqdTNmaz2RYs8Glvb5f2p1KpZVngU8gcRESCIFBDQwNZrVZKJpPKFD5PoXNMTU3JXgPRaJS2bNlCbW1tFI/HiyJD9v78BT4NDQ15V5qvBtwsV4n6+nrau3cvhcNh6u/vp507d8q+gT5//ky7du2icDgsjZ08eZLMZjMFg0EaHh4mm81GNptN9rgul4usVisNDAxQNBolp9NJVVVVJAhCUeWYKxQKLctHRwqdIxqNUnl5OTU1NdHExIS0FeIN/OHDh1RWVkZdXV0Ui8XI6/WSVqulRCJBRETNzc3k8/mk+QMDA6RWq6m9vZ3evHlDfr8/50dHtFotPX36lF6/fk0ul2tZPjpSyByCINDhw4epoqKCXr16Jfu6z87OFk2OXJReDatEhidPntCaNWvozp07NDY2Rp2dnVRaWkp9fX2K5SgUbparxPT0NHk8Hlq/fj1t2LCBjh8/Tt++fZP2j4+PEwAKhULS2I8fP+jUqVO0adMmWrduHbndbpqYmJA9biqVohMnTpBWqyWdTkdut5s+fvxYdDnmWo5mqUQOv99PABZs27ZtK0jNnZ2dZDabSaPRUE1NDb18+VLad+DAATp69Khs/uPHj6myspI0Gg1ZrVbq6emR7RdFkS5evEhGo5HKysqorq6ORkdHC1LrcuXIPk+5trnP3WrPkYvSzZJImQx3796lHTt20Nq1a2nPnj3U3d2taIZC4etZMsYYY3nwaljGGGMsD26WjDHGWB7cLBljjLE8uFkyxhhjeXCzZIwxxvLgZskYY4zlwc2SMcYYy4ObJWOMMZYHN0vGGGMsD26WjDHGWB7cLBljjLE8/gJn646mKU/IgwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "from gem.embedding.hope import HOPE\n",
+    "\n",
+    "hp = HOPE(d=4, beta=0.01)\n",
+    "V=np.matrix(hp.learn_embedding(G))[:, 2:]\n",
+    "\n",
+    "plot_embeddings(V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DeepWalk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 500x500 with 1 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x73d57e2d2fa0>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "from karateclub.node_embedding.neighbourhood.deepwalk import DeepWalk\n",
+    "\n",
+    "dw = DeepWalk(dimensions=2)\n",
+    "dw.fit(G)\n",
+    "\n",
+    "V=np.matrix(dw.get_embedding())\n",
+    "\n",
+    "plot_embeddings(V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Node2Vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|██████████████████████████████| 24/24 [00:00<00:00, 3618.12it/s]\n",
+      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████| 10/10 [00:00<00:00, 23.73it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "from node2vec import Node2Vec\n",
+    "\n",
+    "node2vec = Node2Vec(G, dimensions=2)\n",
+    "model = node2vec.fit(window=10)\n",
+    "embeddings = model.wv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V = np.matrix([model.wv[str(x)] for x in G.nodes()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 500x500 with 1 Axes>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x73d52a2fd400>)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_embeddings(V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Edge2Vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from node2vec.edges import HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges_embs = HadamardEmbedder(keyed_vectors=model.wv)\n",
+    "V = np.matrix([edges_embs[str(x), str(y)] for x, y in G.edges()])\n",
+    "fig, ax = plot_edge_embeddings(V, list(G.edges()))\n",
+    "\n",
+    "ax.set_title(\"HadamardEmbedder\")\n",
+    "plt.savefig(FIGURES_DIR / \"HadamardEmbedder.png\", format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges_embs = AverageEmbedder(keyed_vectors=model.wv)\n",
+    "V = np.matrix([edges_embs[str(x), str(y)] for x, y in G.edges()])\n",
+    "fig, ax = plot_edge_embeddings(V, list(G.edges()))\n",
+    "\n",
+    "ax.set_title(\"AverageEmbedder\")\n",
+    "plt.savefig(FIGURES_DIR / \"AverageEmbedder.png\", format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges_embs = WeightedL1Embedder(keyed_vectors=model.wv)\n",
+    "V = np.matrix([edges_embs[str(x), str(y)] for x, y in G.edges()])\n",
+    "fig, ax = plot_edge_embeddings(V, list(G.edges()))\n",
+    "\n",
+    "ax.set_title(\"WeightedL1Embedder\")\n",
+    "plt.savefig(FIGURES_DIR / \"WeightedL1Embedder.png\", format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "edges_embs = WeightedL2Embedder(keyed_vectors=model.wv)\n",
+    "V = np.matrix([edges_embs[str(x), str(y)] for x, y in G.edges()])\n",
+    "fig, ax = plot_edge_embeddings(V, list(G.edges()))\n",
+    "\n",
+    "ax.set_title(\"WeightedL2Embedder\")\n",
+    "plt.savefig(FIGURES_DIR / \"WeightedL2Embedder.png\", format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graph2Vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from karateclub import Graph2Vec\n",
+    "\n",
+    "n_graphs = 20\n",
+    "\n",
+    "def generate_radom():\n",
+    "    n = random.randint(6, 20)\n",
+    "    k = random.randint(5, n)\n",
+    "    p = random.uniform(0, 1)\n",
+    "    return nx.watts_strogatz_graph(n,k,p), [n,k,p]\n",
+    "\n",
+    "Gs = [generate_radom() for x in range(n_graphs)]\n",
+    "\n",
+    "model = Graph2Vec(dimensions=2, wl_iterations=10)\n",
+    "model.fit([x[0] for x in Gs])\n",
+    "embeddings = model.get_embedding()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,10))\n",
+    "\n",
+    "for i,vec in enumerate(embeddings):\n",
+    "    \n",
+    "    ax.scatter(vec[0],vec[1], s=1000)\n",
+    "    ax.annotate(str(i), (vec[0],vec[1]), fontsize=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap4",
+   "language": "python",
+   "name": "chap4"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter03/02_Autoencoders.ipynb b/Chapter04/02_Autoencoders.ipynb
similarity index 99%
rename from Chapter03/02_Autoencoders.ipynb
rename to Chapter04/02_Autoencoders.ipynb
index 067817f..f718c9e 100644
--- a/Chapter03/02_Autoencoders.ipynb
+++ b/Chapter04/02_Autoencoders.ipynb
@@ -605,9 +605,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap4",
    "language": "python",
-   "name": "python3"
+   "name": "chap4"
   },
   "language_info": {
    "codemirror_mode": {
@@ -619,7 +619,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/Chapter04/03_Structural_deep_neural_embeddings.ipynb b/Chapter04/03_Structural_deep_neural_embeddings.ipynb
new file mode 100644
index 0000000..a136955
--- /dev/null
+++ b/Chapter04/03_Structural_deep_neural_embeddings.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Structural Deep Network Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import DATA_DIR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-23 08:30:36.041513: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-12-23 08:30:36.041534: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gem.embedding.sdne import SDNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = nx.karate_club_graph()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 = SDNE(d=2, beta=5, alpha=1e-5, nu1=1e-6, nu2=1e-6, K=3,n_units=[50, 15,], rho=0.3, n_iter=50, \n",
+    "          xeta=0.01,n_batch=100,\n",
+    "          modelfile=[f'{DATA_DIR}/enc_model.json', f'{DATA_DIR}/dec_model.json'],\n",
+    "          weightfile=[f'{DATA_DIR}/enc_weights.hdf5', f'{DATA_DIR}/dec_weights.hdf5'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-23 08:30:38.747989: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
+      "2023-12-23 08:30:38.748149: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2023-12-23 08:30:38.748157: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-12-23 08:30:38.748173: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (73bfad00a74a): /proc/driver/nvidia/version does not exist\n",
+      "2023-12-23 08:30:38.748290: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX512F\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-12-23 08:30:38.748654: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
+      "/home/euler/.conda/envs/chap3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+      "  warnings.warn('`Model.fit_generator` is deprecated and '\n",
+      "2023-12-23 08:30:38.834684: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
+      "2023-12-23 08:30:38.835029: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2803200000 Hz\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "1/1 [==============================] - 1s 579ms/step - loss: 48.8211 - subtract_loss: 24.4040 - subtract_1_loss: 24.4162 - subtract_2_loss: 0.1179\n",
+      "Epoch 2/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 40.8965 - subtract_loss: 20.4877 - subtract_1_loss: 20.4080 - subtract_2_loss: 0.2633\n",
+      "Epoch 3/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 25.2150 - subtract_loss: 12.3894 - subtract_1_loss: 12.8247 - subtract_2_loss: 0.8324\n",
+      "Epoch 4/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 40.5483 - subtract_loss: 20.2503 - subtract_1_loss: 20.2971 - subtract_2_loss: 2.2028\n",
+      "Epoch 5/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 33.7522 - subtract_loss: 16.9356 - subtract_1_loss: 16.8157 - subtract_2_loss: 0.0167\n",
+      "Epoch 6/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 28.4778 - subtract_loss: 14.2393 - subtract_1_loss: 14.2377 - subtract_2_loss: 0.0055\n",
+      "Epoch 7/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 23.3574 - subtract_loss: 11.7623 - subtract_1_loss: 11.5943 - subtract_2_loss: 0.0037\n",
+      "Epoch 8/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 19.1721 - subtract_loss: 9.4796 - subtract_1_loss: 9.6916 - subtract_2_loss: 0.0029\n",
+      "Epoch 9/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.7146 - subtract_loss: 8.8088 - subtract_1_loss: 8.9050 - subtract_2_loss: 0.0015\n",
+      "Epoch 10/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.9106 - subtract_loss: 8.7734 - subtract_1_loss: 9.1363 - subtract_2_loss: 0.0011\n",
+      "Epoch 11/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.5251 - subtract_loss: 9.2904 - subtract_1_loss: 9.2338 - subtract_2_loss: 0.0020\n",
+      "Epoch 12/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.9172 - subtract_loss: 8.6302 - subtract_1_loss: 9.2861 - subtract_2_loss: 9.6265e-04\n",
+      "Epoch 13/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.3274 - subtract_loss: 8.9781 - subtract_1_loss: 9.3484 - subtract_2_loss: 0.0012\n",
+      "Epoch 14/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.2249 - subtract_loss: 8.9596 - subtract_1_loss: 9.2643 - subtract_2_loss: 0.0014\n",
+      "Epoch 15/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.4546 - subtract_loss: 8.7729 - subtract_1_loss: 8.6807 - subtract_2_loss: 0.0013\n",
+      "Epoch 16/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.3385 - subtract_loss: 8.8069 - subtract_1_loss: 8.5307 - subtract_2_loss: 7.0282e-04\n",
+      "Epoch 17/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.7736 - subtract_loss: 8.0695 - subtract_1_loss: 7.7031 - subtract_2_loss: 7.8459e-04\n",
+      "Epoch 18/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 14.4701 - subtract_loss: 7.1485 - subtract_1_loss: 7.3205 - subtract_2_loss: 8.7221e-04\n",
+      "Epoch 19/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 12.8568 - subtract_loss: 6.4012 - subtract_1_loss: 6.4545 - subtract_2_loss: 7.3244e-04\n",
+      "Epoch 20/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 11.0182 - subtract_loss: 5.3047 - subtract_1_loss: 5.7123 - subtract_2_loss: 0.0013\n",
+      "Epoch 21/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 23.4081 - subtract_loss: 11.7107 - subtract_1_loss: 11.6962 - subtract_2_loss: 0.0028\n",
+      "Epoch 22/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 38.3554 - subtract_loss: 18.4974 - subtract_1_loss: 19.8568 - subtract_2_loss: 0.0080\n",
+      "Epoch 23/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 19.6061 - subtract_loss: 9.6006 - subtract_1_loss: 10.0043 - subtract_2_loss: 0.0141\n",
+      "Epoch 24/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.6101 - subtract_loss: 7.7951 - subtract_1_loss: 7.8137 - subtract_2_loss: 0.0316\n",
+      "Epoch 25/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.0080 - subtract_loss: 7.6025 - subtract_1_loss: 7.4041 - subtract_2_loss: 0.0355\n",
+      "Epoch 26/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.4580 - subtract_loss: 7.4118 - subtract_1_loss: 8.0449 - subtract_2_loss: 0.0433\n",
+      "Epoch 27/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 15.4757 - subtract_loss: 7.6396 - subtract_1_loss: 7.8347 - subtract_2_loss: 0.0702\n",
+      "Epoch 28/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.1886 - subtract_loss: 7.6657 - subtract_1_loss: 7.5214 - subtract_2_loss: 0.0882\n",
+      "Epoch 29/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 14.7275 - subtract_loss: 7.2580 - subtract_1_loss: 7.4681 - subtract_2_loss: 0.0831\n",
+      "Epoch 30/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 14.6846 - subtract_loss: 7.5587 - subtract_1_loss: 7.1243 - subtract_2_loss: 0.1224\n",
+      "Epoch 31/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 14.7579 - subtract_loss: 7.3171 - subtract_1_loss: 7.4392 - subtract_2_loss: 0.1591\n",
+      "Epoch 32/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 14.7877 - subtract_loss: 7.7915 - subtract_1_loss: 6.9945 - subtract_2_loss: 0.2763\n",
+      "Epoch 33/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 14.6301 - subtract_loss: 7.3801 - subtract_1_loss: 7.2483 - subtract_2_loss: 0.2743\n",
+      "Epoch 34/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 15.2862 - subtract_loss: 7.7466 - subtract_1_loss: 7.5378 - subtract_2_loss: 0.3440\n",
+      "Epoch 35/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 16.3416 - subtract_loss: 8.0566 - subtract_1_loss: 8.2832 - subtract_2_loss: 0.3130\n",
+      "Epoch 36/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 19.4905 - subtract_loss: 9.2941 - subtract_1_loss: 10.1945 - subtract_2_loss: 0.3638\n",
+      "Epoch 37/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 22.2789 - subtract_loss: 10.6966 - subtract_1_loss: 11.5804 - subtract_2_loss: 0.3831\n",
+      "Epoch 38/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.3702 - subtract_loss: 8.8701 - subtract_1_loss: 9.4982 - subtract_2_loss: 0.4219\n",
+      "Epoch 39/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.5939 - subtract_loss: 9.1757 - subtract_1_loss: 9.4162 - subtract_2_loss: 0.4154\n",
+      "Epoch 40/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.9261 - subtract_loss: 9.2116 - subtract_1_loss: 9.7124 - subtract_2_loss: 0.4142\n",
+      "Epoch 41/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 18.6240 - subtract_loss: 9.3151 - subtract_1_loss: 9.3068 - subtract_2_loss: 0.4984\n",
+      "Epoch 42/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 18.2632 - subtract_loss: 9.0767 - subtract_1_loss: 9.1843 - subtract_2_loss: 0.4449\n",
+      "Epoch 43/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.4274 - subtract_loss: 9.4235 - subtract_1_loss: 9.0017 - subtract_2_loss: 0.3509\n",
+      "Epoch 44/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 17.8677 - subtract_loss: 8.9676 - subtract_1_loss: 8.8978 - subtract_2_loss: 0.3856\n",
+      "Epoch 45/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.9254 - subtract_loss: 9.2084 - subtract_1_loss: 8.7146 - subtract_2_loss: 0.3850\n",
+      "Epoch 46/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.2363 - subtract_loss: 8.7543 - subtract_1_loss: 9.4796 - subtract_2_loss: 0.4914\n",
+      "Epoch 47/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.5026 - subtract_loss: 8.5746 - subtract_1_loss: 9.9254 - subtract_2_loss: 0.4860\n",
+      "Epoch 48/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 17.2200 - subtract_loss: 8.6719 - subtract_1_loss: 8.5456 - subtract_2_loss: 0.4400\n",
+      "Epoch 49/50\n",
+      "1/1 [==============================] - 0s 2ms/step - loss: 18.1795 - subtract_loss: 8.6737 - subtract_1_loss: 9.5032 - subtract_2_loss: 0.5469\n",
+      "Epoch 50/50\n",
+      "1/1 [==============================] - 0s 1ms/step - loss: 18.0687 - subtract_loss: 8.8354 - subtract_1_loss: 9.2305 - subtract_2_loss: 0.5479\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.008277  , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.        , 0.        ],\n",
+       "       [0.78499395, 0.        ],\n",
+       "       [1.6690726 , 0.        ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1.learn_embedding(graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = list(zip(*m1.get_embedding()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa677101cd0>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y, 'o',linewidth=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap4",
+   "language": "python",
+   "name": "chap4"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter04/04_Graph_Neural_Network.ipynb b/Chapter04/04_Graph_Neural_Network.ipynb
new file mode 100644
index 0000000..527a609
--- /dev/null
+++ b/Chapter04/04_Graph_Neural_Network.ipynb
@@ -0,0 +1,567 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NBVKcDWHeGoR"
+   },
+   "source": [
+    "# Unsupervised graph representation learning using Graph ConvNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lb6FvAQ3eUNs"
+   },
+   "source": [
+    "In this notebook we will be performing unsupervised graph representation learning using Graph ConvNet as encoder.\n",
+    "\n",
+    "The model embeds a graph by using stacked Graph ConvNet layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import draw_graph, FIGURES_DIR\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "RyweACZPHYQA"
+   },
+   "outputs": [],
+   "source": [
+    "#from networkx import karate_club_graph, to_numpy_matrix\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from scipy.linalg import sqrtm\n",
+    "\n",
+    "G = nx.barbell_graph(m1=10, m2=4)\n",
+    "\n",
+    "order = np.arange(G.number_of_nodes())\n",
+    "A = nx.to_numpy_matrix(G, nodelist=order)\n",
+    "I = np.eye(G.number_of_nodes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "JgSsTLzr9a4y"
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(7)\n",
+    "\n",
+    "A_hat = A + np.eye(G.number_of_nodes()) # add self-connections\n",
+    "\n",
+    "D_hat = np.array(np.sum(A_hat, axis=0))[0]\n",
+    "D_hat = np.array(np.diag(D_hat))\n",
+    "D_hat = np.linalg.inv(sqrtm(D_hat))\n",
+    "\n",
+    "A_hat = D_hat @ A_hat @ D_hat\n",
+    "\n",
+    "def glorot_init(nin, nout):\n",
+    "  sd = np.sqrt(6.0 / (nin + nout))\n",
+    "  return np.random.uniform(-sd, sd, size=(nin, nout))\n",
+    "\n",
+    "class GCNLayer():\n",
+    "  def __init__(self, n_inputs, n_outputs):\n",
+    "      self.n_inputs = n_inputs\n",
+    "      self.n_outputs = n_outputs\n",
+    "      self.W = glorot_init(self.n_outputs, self.n_inputs)\n",
+    "      self.activation = np.tanh\n",
+    "      \n",
+    "  def forward(self, A, X):\n",
+    "      self._X = (A @ X).T # (N,N)*(N,n_outputs) ==> (n_outputs,N)\n",
+    "      H = self.W @ self._X # (N, D)*(D, n_outputs) => (N, n_outputs)\n",
+    "      H = self.activation(H)\n",
+    "      return H.T # (n_outputs, N)\n",
+    "\n",
+    "gcn1 = GCNLayer(G.number_of_nodes(), 8)\n",
+    "gcn2 = GCNLayer(8, 4)\n",
+    "gcn3 = GCNLayer(4, 2)\n",
+    "\n",
+    "H1 = gcn1.forward(A_hat, I)\n",
+    "H2 = gcn2.forward(A_hat, H1)\n",
+    "H3 = gcn3.forward(A_hat, H2)\n",
+    "\n",
+    "embeddings = H3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 319
+    },
+    "id": "OhVzlenz1x97",
+    "outputId": "a6659970-a1e6-4b7a-a6a6-e0903ceefe5f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "embeddings = np.array(embeddings)\n",
+    "draw_graph(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 265
+    },
+    "id": "XLgjmzRLLLcs",
+    "outputId": "d056de50-0e08-49f8-ea79-fc0c4bc52778"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(embeddings[:, 0], embeddings[:, 1])\n",
+    "plt.savefig(f'{FIGURES_DIR}/embedding_gcn.png',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C83YCCDLG-Cv"
+   },
+   "source": [
+    "## Unsupervised GCN training using similarity graph distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lpDNVIR8fMJT"
+   },
+   "source": [
+    "For the next example, we need to install StellarGraph, the python library we will be using to build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "iafwVXyrL6q6"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-23 08:37:14.102512: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-12-23 08:37:14.102531: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "2023-12-23 08:37:14.761469: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
+      "2023-12-23 08:37:14.761564: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2023-12-23 08:37:14.761571: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-12-23 08:37:14.761584: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (73bfad00a74a): /proc/driver/nvidia/version does not exist\n",
+      "2023-12-23 08:37:14.761747: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX512F\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-12-23 08:37:14.762464: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n"
+     ]
+    }
+   ],
+   "source": [
+    "import stellargraph as sg\n",
+    "from stellargraph.mapper import FullBatchNodeGenerator\n",
+    "from stellargraph.layer import GCN\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
+    "from sklearn import preprocessing, model_selection\n",
+    "from IPython.display import display, HTML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VHU1UGiHfw1e"
+   },
+   "source": [
+    "In this demo, we will be using the PROTEINS dataset, already integrated in StellarGraph (although we need to override the url, given that the original data was removed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 68
+    },
+    "id": "zhttMYjFMu5f",
+    "outputId": "15cf0fdd-8eec-41eb-b307-3e26575b692a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Each graph represents a protein and graph labels represent whether they are are enzymes or non-enzymes. The dataset includes 1113 graphs with 39 nodes and 73 edges on average for each graph. Graph nodes have 4 attributes (including a one-hot encoding of their label), and each graph is labelled as belonging to 1 of 2 classes."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sg.datasets.PROTEINS.url = 'https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip'\n",
+    "dataset = sg.datasets.PROTEINS()\n",
+    "display(HTML(dataset.description))\n",
+    "graphs, graph_labels = dataset.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 315
+    },
+    "id": "n1A345-rMx8V",
+    "outputId": "3d31a583-a43d-4478-bddc-c4da1c109228"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 42, Edges: 162\n",
+      "\n",
+      " Node types:\n",
+      "  default: [42]\n",
+      "    Features: float32 vector, length 4\n",
+      "    Edge types: default-default->default\n",
+      "\n",
+      " Edge types:\n",
+      "    default-default->default: [162]\n",
+      "        Weights: all 1 (default)\n",
+      "        Features: none\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>450</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   label\n",
+       "1    663\n",
+       "2    450"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# let's print some info to better understand the dataset\n",
+    "print(graphs[0].info())\n",
+    "graph_labels.value_counts().to_frame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tVx9OQoSgViY"
+   },
+   "source": [
+    "### Model definition\n",
+    "It's now time to build-up the model. StellarGraph offers several utility function to load and process the dataset, as well as define the GNN model and train."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "gn1egwLSgUd3"
+   },
+   "outputs": [],
+   "source": [
+    "# TODO\n",
+    "generator = sg.mapper.PaddedGraphGenerator(graphs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "vBJo0MkBNCLE"
+   },
+   "outputs": [],
+   "source": [
+    "# define a GCN model containing 2 layers of size 64 and 32, respectively. \n",
+    "# ReLU activation function is used to add non-linearity between layers\n",
+    "gc_model = sg.layer.GCNSupervisedGraphClassification(\n",
+    "    [64, 32], [\"relu\", \"relu\"], generator, pool_all_layers=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "6WYIXEO1NHdW"
+   },
+   "outputs": [],
+   "source": [
+    "inp1, out1 = gc_model.in_out_tensors()\n",
+    "inp2, out2 = gc_model.in_out_tensors()\n",
+    "\n",
+    "vec_distance = tf.norm(out1 - out2, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "dG5WFf7LNWTL"
+   },
+   "outputs": [],
+   "source": [
+    "pair_model = Model(inp1 + inp2, vec_distance)\n",
+    "embedding_model = Model(inp1, out1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "liCd_C-JKebp"
+   },
+   "outputs": [],
+   "source": [
+    "def graph_distance(graph1, graph2):\n",
+    "    spec1 = nx.laplacian_spectrum(graph1.to_networkx(feature_attr=None))\n",
+    "    spec2 = nx.laplacian_spectrum(graph2.to_networkx(feature_attr=None))\n",
+    "    k = min(len(spec1), len(spec2))\n",
+    "    return np.linalg.norm(spec1[:k] - spec2[:k])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "wN0RSDgSKtVM"
+   },
+   "outputs": [],
+   "source": [
+    "graph_idx = np.random.RandomState(0).randint(len(graphs), size=(100, 2))\n",
+    "targets = [graph_distance(graphs[left], graphs[right]) for left, right in graph_idx]\n",
+    "train_gen = generator.flow(graph_idx, batch_size=10, targets=targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "HQpoEAdvKzWL"
+   },
+   "outputs": [],
+   "source": [
+    "pair_model.compile(optimizers.Adam(1e-2), loss=\"mse\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "id": "aYL3qZXYLGrX",
+    "outputId": "fd0867e2-6eae-47d4-80b7-07ad5d9485e3"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-12-23 08:37:19.486366: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
+      "2023-12-23 08:37:19.486672: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2803200000 Hz\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "history = pair_model.fit(train_gen, epochs=500, verbose=0)\n",
+    "sg.utils.plot_history(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "oArvDvO3LOXc"
+   },
+   "outputs": [],
+   "source": [
+    "embeddings = embedding_model.predict(generator.flow(graphs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "jDEfCnALMFm2"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "tsne = TSNE(2)\n",
+    "two_d = tsne.fit_transform(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 267
+    },
+    "id": "6XUWp7ZzMMtC",
+    "outputId": "2f2702c9-ab2e-424a-e6eb-d72c45eef4f5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(two_d[:, 0], two_d[:, 1], c=graph_labels.cat.codes, cmap=\"jet\", alpha=0.4)\n",
+    "plt.savefig(f'{FIGURES_DIR}/embedding_TSNE.png',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "i34QgSA_P_sM"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Unsupervised_GraphML.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "chap4",
+   "language": "python",
+   "name": "chap4"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter04/poetry.lock b/Chapter04/poetry.lock
new file mode 100644
index 0000000..38ed5db
--- /dev/null
+++ b/Chapter04/poetry.lock
@@ -0,0 +1,2243 @@
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
+version = "0.15.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+optional = false
+python-versions = "*"
+files = [
+    {file = "absl-py-0.15.0.tar.gz", hash = "sha256:72d782fbeafba66ba3e525d46bccac949b9a174dbf66233e50ece09ee688dc81"},
+    {file = "absl_py-0.15.0-py3-none-any.whl", hash = "sha256:ea907384af023a7e681368bedb896159ab100c7db593efbbd5cde22af11270cd"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
+files = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
+    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
+]
+
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+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "771516ece2620e992a3d0c44cf8fda7f65a198370eacae06a23748331976df95"
diff --git a/Chapter04/pyproject.toml b/Chapter04/pyproject.toml
new file mode 100644
index 0000000..d1e24e7
--- /dev/null
+++ b/Chapter04/pyproject.toml
@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 4"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+networkx = "==2.5" 
+matplotlib = "==3.2.2"
+node2vec = "==0.3.3"
+karateclub = "==1.0.19"
+gensim = "==3.8.3"
+scikit-learn = "==0.24.0" 
+tensorflow = "==2.4.0" 
+nxt-gem = { git="https://github.com/palash1992/GEM.git", branch="master" }
+# This is what is holding us back to python 3.8
+stellargraph = { git="https://github.com/stellargraph/stellargraph.git", branch="develop" }
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Chapter04/requirements.txt b/Chapter04/requirements.txt
new file mode 100644
index 0000000..99b00bc
--- /dev/null
+++ b/Chapter04/requirements.txt
@@ -0,0 +1,96 @@
+absl-py==0.15.0 ; python_version >= "3.8" and python_version < "3.9"
+appnope==0.1.3 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+cachetools==5.3.2 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2023.11.17 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.16.0 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+charset-normalizer==3.3.2 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+cython==0.29.14 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.0 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+executing==2.0.1 ; python_version >= "3.8" and python_version < "3.9"
+flatbuffers==1.12 ; python_version >= "3.8" and python_version < "3.9"
+gast==0.3.3 ; python_version >= "3.8" and python_version < "3.9"
+gensim==3.8.3 ; python_version >= "3.8" and python_version < "3.9"
+google-auth-oauthlib==0.4.6 ; python_version >= "3.8" and python_version < "3.9"
+google-auth==2.25.2 ; python_version >= "3.8" and python_version < "3.9"
+google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+grpcio==1.32.0 ; python_version >= "3.8" and python_version < "3.9"
+h5py==2.10.0 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.6 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==7.0.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.27.1 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.1 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.3.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.0 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.5.1 ; python_version >= "3.8" and python_version < "3.9"
+karateclub==1.0.19 ; python_version >= "3.8" and python_version < "3.9"
+keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.5 ; python_version >= "3.8" and python_version < "3.9"
+markdown==3.5.1 ; python_version >= "3.8" and python_version < "3.9"
+markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.6 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.5.8 ; python_version >= "3.8" and python_version < "3.9"
+networkx==2.5 ; python_version >= "3.8" and python_version < "3.9"
+node2vec==0.3.3 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.19.5 ; python_version >= "3.8" and python_version < "3.9"
+nxt-gem @ git+https://github.com/palash1992/GEM.git@master ; python_version >= "3.8" and python_version < "3.9"
+oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+opt-einsum==3.3.0 ; python_version >= "3.8" and python_version < "3.9"
+packaging==23.2 ; python_version >= "3.8" and python_version < "3.9"
+pandas==1.4.4 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.3 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.1.0 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.43 ; python_version >= "3.8" and python_version < "3.9"
+protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9"
+psutil==5.9.7 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1-modules==0.3.0 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1==0.5.1 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.21 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pygments==2.17.2 ; python_version >= "3.8" and python_version < "3.9"
+pygsp==0.5.1 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.1 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.8.2 ; python_version >= "3.8" and python_version < "3.9"
+python-louvain==0.16 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2023.3.post1 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyzmq==25.1.2 ; python_version >= "3.8" and python_version < "3.9"
+requests-oauthlib==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.31.0 ; python_version >= "3.8" and python_version < "3.9"
+rsa==4.9 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==0.24.0 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+setuptools==69.0.2 ; python_version >= "3.8" and python_version < "3.9"
+six==1.15.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==6.4.0 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+stellargraph @ git+https://github.com/stellargraph/stellargraph.git@develop ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-data-server==0.6.1 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-plugin-wit==1.8.1 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard==2.11.2 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-estimator==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+termcolor==1.1.0 ; python_version >= "3.8" and python_version < "3.9"
+theano==1.0.5 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.2.0 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.66.1 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.0 ; python_version >= "3.8" and python_version < "3.9"
+typing-extensions==3.7.4.3 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.12 ; python_version >= "3.8" and python_version < "3.9"
+werkzeug==3.0.1 ; python_version >= "3.8" and python_version < "3.9"
+wheel==0.42.0 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.12.1 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.17.0 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter04/01_Feature_based_methods.ipynb b/Chapter05/01_Feature_based_methods.ipynb
similarity index 96%
rename from Chapter04/01_Feature_based_methods.ipynb
rename to Chapter05/01_Feature_based_methods.ipynb
index 47cfa80..38ebc24 100644
--- a/Chapter04/01_Feature_based_methods.ipynb
+++ b/Chapter05/01_Feature_based_methods.ipynb
@@ -56,6 +56,8 @@
     "from stellargraph import datasets\n",
     "from IPython.display import display, HTML\n",
     "\n",
+    "datasets.PROTEINS.url = 'https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip'\n",
+    "\n",
     "dataset = datasets.PROTEINS()\n",
     "display(HTML(dataset.description))\n",
     "graphs, graph_labels = dataset.load()"
@@ -211,9 +213,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap5",
    "language": "python",
-   "name": "python3"
+   "name": "chap5"
   },
   "language_info": {
    "codemirror_mode": {
@@ -225,9 +227,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.8.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Chapter05/01_link_prediction.ipynb b/Chapter05/01_link_prediction.ipynb
deleted file mode 100644
index dfb7cf5..0000000
--- a/Chapter05/01_link_prediction.ipynb
+++ /dev/null
@@ -1,388 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Similarity Based Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Index Based"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Resource Allocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 2, 0.5), (2, 5, 0.5), (3, 4, 0.5)]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
-    "G = nx.from_edgelist(edges)\n",
-    "preds = nx.resource_allocation_index(G,[(1,2),(2,5),(3,4)])\n",
-    "print(list(preds))\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Jaccard Coefficient"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 2, 0.5), (2, 5, 0.25), (3, 4, 0.3333333333333333)]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
-    "G = nx.from_edgelist(edges)\n",
-    "preds = nx.jaccard_coefficient(G,[(1,2),(2,5),(3,4)])\n",
-    "print(list(preds))\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Community Based"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Community Common Neighbor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 2, 2), (2, 5, 1), (3, 4, 1)]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
-    "G = nx.from_edgelist(edges)\n",
-    "\n",
-    "G.nodes[1][\"community\"] = 0\n",
-    "G.nodes[2][\"community\"] = 0\n",
-    "G.nodes[3][\"community\"] = 0\n",
-    "\n",
-    "G.nodes[4][\"community\"] = 1\n",
-    "G.nodes[5][\"community\"] = 1\n",
-    "G.nodes[6][\"community\"] = 1\n",
-    "G.nodes[7][\"community\"] = 1\n",
-    "preds = nx.cn_soundarajan_hopcroft(G,[(1,2),(2,5),(3,4)])\n",
-    "print(list(preds))\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Community Common Neighbor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 2, 0.5), (2, 5, 0), (3, 4, 0)]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
-    "G = nx.from_edgelist(edges)\n",
-    "\n",
-    "G.nodes[1][\"community\"] = 0\n",
-    "G.nodes[2][\"community\"] = 0\n",
-    "G.nodes[3][\"community\"] = 0\n",
-    "\n",
-    "G.nodes[4][\"community\"] = 1\n",
-    "G.nodes[5][\"community\"] = 1\n",
-    "G.nodes[6][\"community\"] = 1\n",
-    "G.nodes[7][\"community\"] = 1\n",
-    "preds = nx.ra_index_soundarajan_hopcroft(G,[(1,2),(2,5),(3,4)])\n",
-    "print(list(preds))\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Embedding based"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import networkx as nx\n",
-    "import pandas as pd\n",
-    "\n",
-    "edgelist = pd.read_csv(\"cora.cites\", sep='\\t', header=None, names=[\"target\", \"source\"])\n",
-    "G = nx.from_pandas_edgelist(edgelist)\n",
-    "draw_graph(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "** Sampled 527 positive and 527 negative edges. **\n"
-     ]
-    }
-   ],
-   "source": [
-    "from stellargraph.data import EdgeSplitter\n",
-    "\n",
-    "edgeSplitter = EdgeSplitter(G)\n",
-    "graph_test, samples_test, labels_test = edgeSplitter.train_test_split(\n",
-    "    p=0.1, method=\"global\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "** Sampled 475 positive and 475 negative edges. **\n"
-     ]
-    }
-   ],
-   "source": [
-    "edgeSplitter = EdgeSplitter(graph_test, G)\n",
-    "graph_train, samples_train, labels_train = edgeSplitter.train_test_split(\n",
-    "    p=0.1, method=\"global\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing transition probabilities: 100%|██████████| 2708/2708 [00:00<00:00, 4284.35it/s]\n",
-      "Generating walks (CPU: 1): 100%|██████████| 10/10 [01:24<00:00,  8.43s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from node2vec import Node2Vec\n",
-    "from node2vec.edges import HadamardEmbedder\n",
-    "\n",
-    "node2vec = Node2Vec(graph_train)\n",
-    "model = node2vec.fit()\n",
-    "edges_embs = HadamardEmbedder(keyed_vectors=model.wv)\n",
-    "train_embeddings = [edges_embs[str(x[0]),str(x[1])] for x in samples_train]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_embeddings = [edges_embs[str(x[0]),str(x[1])] for x in samples_test]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "rf = RandomForestClassifier(n_estimators=1000)\n",
-    "rf.fit(train_embeddings, labels_train);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Precision: 0.8557114228456913\n",
-      "Recall: 0.8102466793168881\n",
-      "F1-Score: 0.8323586744639375\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn import metrics\n",
-    "\n",
-    "y_pred = rf.predict(test_embeddings)\n",
-    "\n",
-    "print('Precision:', metrics.precision_score(labels_test, y_pred))\n",
-    "print('Recall:', metrics.recall_score(labels_test, y_pred))\n",
-    "print('F1-Score:', metrics.f1_score(labels_test, y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "def draw_graph(G, node_names={}, node_size=500):\n",
-    "    pos_nodes = nx.spring_layout(G)\n",
-    "    nx.draw(G, pos_nodes, with_labels=True, node_size=node_size, edge_color='gray', arrowsize=30)\n",
-    "    \n",
-    "    pos_attrs = {}\n",
-    "    for node, coords in pos_nodes.items():\n",
-    "        pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
-    "        \n",
-    "    #nx.draw_networkx_labels(G, pos_attrs, font_family='serif', font_size=20)\n",
-    "    \n",
-    "    plt.axis('off')\n",
-    "    axis = plt.gca()\n",
-    "    axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
-    "    axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
-    "    plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter04/02_Shallow_embeddings.ipynb b/Chapter05/02_Shallow_embeddings.ipynb
similarity index 96%
rename from Chapter04/02_Shallow_embeddings.ipynb
rename to Chapter05/02_Shallow_embeddings.ipynb
index 4d72379..ea3d0ad 100644
--- a/Chapter04/02_Shallow_embeddings.ipynb
+++ b/Chapter05/02_Shallow_embeddings.ipynb
@@ -220,9 +220,11 @@
     "        c_tool = 10\n",
     "        \n",
     "        while it < self.max_iter & c_tool > self.tol:\n",
-    "            Y = A*Y_prev\n",
-    "            #force labeled nodes\n",
-    "            Y[labeled_index] = Y0[labeled_index]\n",
+    "            Y = A * Y_prev\n",
+    "            Y = Y / Y.sum(axis=1)  # Normalize rows to sum to 1\n",
+    "            Y[np.isnan(Y)] = 0 # NaN may arise because of all-zeros rows\n",
+    "            \n",
+    "            Y[labeled_index] = Y0[labeled_index] #force labeled nodes\n",
     "            \n",
     "            it +=1\n",
     "            c_tol = np.sum(np.abs(Y-Y_prev))\n",
@@ -396,6 +398,9 @@
     "        while it < self.max_iter & c_tool > self.tol:\n",
     "            Y = self.alpha*(L*Y_prev)+((1-self.alpha)*Y0)\n",
     "\n",
+    "            Y = Y / Y.sum(axis=1)  # Normalize rows to sum to 1\n",
+    "            Y[np.isnan(Y)] = 0 # NaN may arise because of all-zeros rows\n",
+    "\n",
     "            it +=1\n",
     "            c_tol = np.sum(np.abs(Y-Y_prev))\n",
     "            Y_prev = Y\n",
@@ -429,9 +434,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap5",
    "language": "python",
-   "name": "python3"
+   "name": "chap5"
   },
   "language_info": {
    "codemirror_mode": {
@@ -443,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.8.14"
   }
  },
  "nbformat": 4,
diff --git a/Chapter05/02_community_detection_algorithms.ipynb b/Chapter05/02_community_detection_algorithms.ipynb
deleted file mode 100644
index e773004..0000000
--- a/Chapter05/02_community_detection_algorithms.ipynb
+++ /dev/null
@@ -1,546 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Network Communities Detection "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook, we will explore some methods to perform a community detection using several algortihms. Before testing the algorithms, let us create a simple benchmark graph. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx \n",
-    "G = nx.barbell_graph(m1=10, m2=4) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Matrix Factorization "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by using some matrix factorization technique to extract the embeddings, which are visualized and then clustered traditional clustering algorithms.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SVD error (low rank): 0.052092\n"
-     ]
-    }
-   ],
-   "source": [
-    "from gem.embedding.hope import HOPE \n",
-    "gf = HOPE(d=4, beta=0.01) \n",
-    "gf.learn_embedding(G) \n",
-    "embeddings = gf.get_embedding() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.manifold import TSNE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tsne = TSNE(n_components=2) \n",
-    "\n",
-    "emb2d = tsne.fit_transform(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x123e97880>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(embeddings[:, 0], embeddings[:, 1], 'o', linewidth=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by using a GaussianMixture model to perform the clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.mixture import GaussianMixture"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gm = GaussianMixture(n_components=3, random_state=0) #.(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = gm.fit_predict(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"blue\", \"green\", \"red\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nx.draw_spring(G, node_color=[colors[label] for label in labels])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spectral Clustering"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now perform a spectral clustering based on the adjacency matrix of the graph. It is worth noting that this clustering is not a mutually exclusive clustering and nodes may belong to more than one community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adj=np.array(nx.adjacency_matrix(G).todense())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from communities.algorithms import spectral_clustering\n",
-    "\n",
-    "communities = spectral_clustering(adj, k=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next plot we highlight the nodes that belong to a community using the red color. The blue nodes do not belong to the given community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20, 5))\n",
-    "\n",
-    "for ith, community in enumerate(communities):\n",
-    "    cols = [\"red\" if node in community else \"blue\" for node in G.nodes]\n",
-    "    plt.subplot(1,3,ith+1)\n",
-    "    plt.title(f\"Community {ith}\")\n",
-    "    nx.draw_spring(G, node_color=cols)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next command shows the node ids belonging to the different communities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{14, 15, 16, 17, 18, 19, 20, 21, 22, 23},\n",
-       " {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n",
-       " {12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "communities"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Non Negative Matrix Factorization "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we again use matrix factorization, but now using the Non-Negative Matrix Factorization, and associating the clusters with the latent dimensions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.decomposition import NMF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nmf = NMF(n_components=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.8.6/envs/ml-book-5/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py:312: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n",
-      "  warnings.warn((\"The 'init' value, when 'init=None' and \"\n"
-     ]
-    }
-   ],
-   "source": [
-    "emb = nmf.fit_transform(adj)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1264a0400>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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L+ZV23RMGrFFVPV9V66tqc1VtZu51hh1VdSV94W2Xf2uHmDs7J8l65i7BLPm9wY3pskY/Ad4DkOStzAV9diWHHNmg966J3wUcAR4HDlbV8ST3JtnR2+3fgTckOQV8FFj0LWkt6rhG+4HXAl9N8oMk838Im9Zxja5oHdfoCPBskhPAQ8Deqrpi/jfccY0+BtyZ5IfAV4APrfQJph/9l6RGjOwZuiTp4hh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRvwflchCXFUpv+IAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(emb[:, 0], emb[:, 1], 'o', linewidth=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By setting a threshold value of 0.01, we determine which nodes belong to the given community."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities = [set(np.where(emb[:,ith]>0.01)[0]) for ith in range(2)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20, 5))\n",
-    "\n",
-    "for ith, community in enumerate(communities):\n",
-    "    cols = [\"red\" if node in community else \"blue\" for node in G.nodes]\n",
-    "    plt.subplot(1,3,ith+1)\n",
-    "    plt.title(f\"Community {ith}\")\n",
-    "    nx.draw_spring(G, node_color=cols)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although the example above does not show this, in general also this clustering method may be non-mutually exclusive, and nodes may belong to more than one community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Louvain and Modularity Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we use the Louvain method, which is one of the most popular methods for performing community detection, even on fairly large graphs. As described in the chapter, the Louvain method basically optimize the partitioning (it is a mutually exclusing community detection algorithm), identifying the one that maximize the modularity score, meaning that nodes belonging to the same community are very well connected among themself, and weakly connected to the other communities. \n",
-    "\n",
-    "**Louvain, unlike other community detection algorithms, does not require to specity the number of communities in advance and find the best, optimal number of communities.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from communities.algorithms import louvain_method\n",
-    "communities = louvain_method(adj)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values\n",
-    "nx.draw_spring(G, node_color=c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Girvan Newman"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Girvan–Newman algorithm detects communities by progressively removing edges from the original graph. The algorithm removes the “most valuable” edge, traditionally the edge with the highest betweenness centrality, at each step. As the graph breaks down into pieces, the tightly knit community structure is exposed and the result can be depicted as a dendrogram.\n",
-    "\n",
-    "**BE AWARE that because of the betweeness centrality computation, this method may not scale well on large graphs**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from communities.algorithms import girvan_newman\n",
-    "communities = girvan_newman(adj, n=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values\n",
-    "nx.draw_spring(G, node_color=c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ml-book-5",
-   "language": "python",
-   "name": "ml-book-5"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter04/03_Graph_regularization_graph_neural_training.ipynb b/Chapter05/03_Graph_regularization_graph_neural_training.ipynb
similarity index 99%
rename from Chapter04/03_Graph_regularization_graph_neural_training.ipynb
rename to Chapter05/03_Graph_regularization_graph_neural_training.ipynb
index 8f0fa92..9b17dad 100644
--- a/Chapter04/03_Graph_regularization_graph_neural_training.ipynb
+++ b/Chapter05/03_Graph_regularization_graph_neural_training.ipynb
@@ -1486,9 +1486,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ml-book-4",
+   "display_name": "chap5",
    "language": "python",
-   "name": "ml-book-4"
+   "name": "chap5"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1500,7 +1500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/Chapter04/04_Graph_Neural_Networks.ipynb b/Chapter05/04_Graph_Neural_Networks.ipynb
similarity index 79%
rename from Chapter04/04_Graph_Neural_Networks.ipynb
rename to Chapter05/04_Graph_Neural_Networks.ipynb
index 81d3fe7..f648c56 100644
--- a/Chapter04/04_Graph_Neural_Networks.ipynb
+++ b/Chapter05/04_Graph_Neural_Networks.ipynb
@@ -20,27 +20,6 @@
     "In Chapter 1 you learned how local and global graph properties can be extracted from graphs. Those properties represent the graph itself and bring important informations which can be useful for classification."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "id": "5k3sYIRJpMgb",
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Uninstalling stellargraph-1.2.1:\r\n",
-      "  Successfully uninstalled stellargraph-1.2.1\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install stellargraph"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -66,6 +45,8 @@
     "from stellargraph import datasets\n",
     "from IPython.display import display, HTML\n",
     "\n",
+    "datasets.PROTEINS.url = 'https://www.chrsmrrs.com/graphkerneldatasets/PROTEINS.zip'\n",
+    "\n",
     "dataset = datasets.PROTEINS()\n",
     "display(HTML(dataset.description))\n",
     "graphs, graph_labels = dataset.load()"
@@ -834,7 +815,7 @@
    "source": [
     "from tensorflow.keras.losses import categorical_crossentropy\n",
     "from keras.models import Model\n",
-    "from keras.optimizers import Adam\n",
+    "from tensorflow.keras.optimizers import Adam\n",
     "\n",
     "model = Model(inputs=gnn_inp, outputs=outputs)\n",
     "model.compile(optimizer=Adam(lr=0.003), loss=categorical_crossentropy, metrics=[\"acc\"],)"
@@ -898,6 +879,228 @@
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the rest of the notebook, we will be performing a similar example as above using other two popular graph-dl frameworks: PyTorch Geometric (PyG) and Deep Graph Library (DGL)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Graph Classification using PyG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch_geometric.datasets import TUDataset\n",
+    "from torch_geometric.data import DataLoader\n",
+    "from torch_geometric.nn import GCNConv, global_mean_pool\n",
+    "from torch.nn import Linear\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# Load the PROTEINS dataset\n",
+    "dataset = TUDataset(root='data/PROTEINS', name='PROTEINS')\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "torch.manual_seed(42)\n",
+    "\n",
+    "# Shuffle and split the dataset into training and test sets\n",
+    "dataset = dataset.shuffle()\n",
+    "split_idx = int(0.8 * len(dataset))  # 80/20 train/test split\n",
+    "train_dataset = dataset[:split_idx]\n",
+    "test_dataset = dataset[split_idx:]\n",
+    "\n",
+    "# Print dataset statistics\n",
+    "print(f'Training graphs: {len(train_dataset)}, Test graphs: {len(test_dataset)}')\n",
+    "\n",
+    "# Create DataLoader for batching\n",
+    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
+    "\n",
+    "# Define the GCN model\n",
+    "class GCN(torch.nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
+    "        super(GCN, self).__init__()\n",
+    "        self.conv1 = GCNConv(input_dim, hidden_dim)\n",
+    "        self.conv2 = GCNConv(hidden_dim, hidden_dim)\n",
+    "        self.conv3 = GCNConv(hidden_dim, hidden_dim)\n",
+    "        self.lin = Linear(hidden_dim, output_dim)\n",
+    "        \n",
+    "    def forward(self, x, edge_index, batch):\n",
+    "        # Graph convolution layers with ReLU activations\n",
+    "        x = F.relu(self.conv1(x, edge_index))\n",
+    "        x = F.relu(self.conv2(x, edge_index))\n",
+    "        x = self.conv3(x, edge_index)\n",
+    "        \n",
+    "        # Global pooling to obtain graph-level representation\n",
+    "        x = global_mean_pool(x, batch)\n",
+    "        \n",
+    "        # Apply dropout and final linear layer\n",
+    "        x = F.dropout(x, p=0.5, training=self.training)\n",
+    "        x = self.lin(x)\n",
+    "        return x\n",
+    "\n",
+    "# Instantiate the model\n",
+    "print(dataset.num_node_features)\n",
+    "model = GCN(input_dim=dataset.num_node_features, hidden_dim=64, output_dim=dataset.num_classes)\n",
+    "print(model)\n",
+    "\n",
+    "# Define optimizer and loss function\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5)  # Learning rate decay\n",
+    "\n",
+    "# Training function\n",
+    "def train():\n",
+    "    model.train()\n",
+    "    total_loss = 0\n",
+    "    for data in train_loader:\n",
+    "        optimizer.zero_grad()\n",
+    "        out = model(data.x, data.edge_index, data.batch)\n",
+    "        loss = criterion(out, data.y)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        total_loss += loss.item()\n",
+    "    return total_loss / len(train_loader)\n",
+    "\n",
+    "# Evaluation function\n",
+    "def evaluate(loader):\n",
+    "    model.eval()\n",
+    "    correct = 0\n",
+    "    for data in loader:\n",
+    "        with torch.no_grad():\n",
+    "            out = model(data.x, data.edge_index, data.batch)\n",
+    "            pred = out.argmax(dim=1)\n",
+    "            correct += int((pred == data.y).sum())\n",
+    "    return correct / len(loader.dataset)\n",
+    "\n",
+    "# Training loop\n",
+    "num_epochs = 200\n",
+    "for epoch in range(1, num_epochs + 1):\n",
+    "    loss = train()\n",
+    "    train_acc = evaluate(train_loader)\n",
+    "    test_acc = evaluate(test_loader)\n",
+    "    scheduler.step()  # Adjust learning rate\n",
+    "\n",
+    "    print(f'Epoch {epoch:03d}, Loss: {loss:.4f}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Graph Classification using DGL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import dgl\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from torch.nn import Linear\n",
+    "from dgl.data import GINDataset\n",
+    "from dgl.dataloading import GraphDataLoader\n",
+    "from dgl.nn.pytorch import GraphConv\n",
+    "from dgl.data.utils import split_dataset\n",
+    "\n",
+    "dataset = dgl.data.GINDataset('PROTEINS', self_loop=True)\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "torch.manual_seed(42)\n",
+    "\n",
+    "# 2. Split dataset into training and test sets\n",
+    "train_dataset, val_dataset, test_dataset = split_dataset(dataset, frac_list=[0.8, 0.1, 0.1], shuffle=False, random_state=42)\n",
+    "\n",
+    "# Print dataset statistics\n",
+    "print(f'Training graphs: {len(train_dataset)}, Test graphs: {len(test_dataset)}')\n",
+    "\n",
+    "# 3. Create DGL DataLoader for batching\n",
+    "train_loader = GraphDataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+    "test_loader = GraphDataLoader(test_dataset, batch_size=64, shuffle=False)\n",
+    "\n",
+    "# 4. Define the GCN model using DGL's GraphConv layers\n",
+    "class GCN(torch.nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
+    "        super(GCN, self).__init__()\n",
+    "        self.conv1 = GraphConv(input_dim, hidden_dim)\n",
+    "        self.conv2 = GraphConv(hidden_dim, hidden_dim)\n",
+    "        self.conv3 = GraphConv(hidden_dim, hidden_dim)\n",
+    "        self.fc = Linear(hidden_dim, output_dim)\n",
+    "\n",
+    "    def forward(self, g, features):\n",
+    "        # Apply GraphConv layers with ReLU activations\n",
+    "        h = F.relu(self.conv1(g, features))\n",
+    "        h = F.relu(self.conv2(g, h))\n",
+    "        h = self.conv3(g, h)\n",
+    "        \n",
+    "        # Global mean pooling to obtain graph-level representation\n",
+    "        with g.local_scope():\n",
+    "            g.ndata['h'] = h\n",
+    "            hg = dgl.mean_nodes(g, 'h')\n",
+    "        \n",
+    "        # Apply dropout and final linear layer for classification\n",
+    "        hg = F.dropout(hg, p=0.5, training=self.training)\n",
+    "        return self.fc(hg)\n",
+    "\n",
+    "# 5. Initialize the model, optimizer, and loss function\n",
+    "input_dim = dataset.dim_nfeats\n",
+    "output_dim = dataset.num_classes\n",
+    "hidden_dim = 64\n",
+    "\n",
+    "print(\"Input dim:\", input_dim)\n",
+    "print(\"Output dim:\", output_dim)\n",
+    "\n",
+    "model = GCN(input_dim, hidden_dim, output_dim)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
+    "criterion = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "# 6. Training function\n",
+    "def train():\n",
+    "    model.train()\n",
+    "    total_loss = 0\n",
+    "    for batched_graph, labels in train_loader:\n",
+    "        optimizer.zero_grad()\n",
+    "        features = batched_graph.ndata['attr']\n",
+    "        out = model(batched_graph, features)\n",
+    "        loss = criterion(out, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        total_loss += loss.item()\n",
+    "    return total_loss / len(train_loader)\n",
+    "\n",
+    "# 7. Evaluation function\n",
+    "def evaluate(loader):\n",
+    "    model.eval()\n",
+    "    correct = 0\n",
+    "    for batched_graph, labels in loader:\n",
+    "        features = batched_graph.ndata['attr']\n",
+    "        with torch.no_grad():\n",
+    "            out = model(batched_graph, features)\n",
+    "            pred = out.argmax(dim=1)\n",
+    "            correct += (pred == labels).sum().item()\n",
+    "    return correct / len(loader.dataset)\n",
+    "\n",
+    "# 8. Training loop\n",
+    "num_epochs = 200\n",
+    "for epoch in range(1, num_epochs + 1):\n",
+    "    loss = train()\n",
+    "    train_acc = evaluate(train_loader)\n",
+    "    test_acc = evaluate(test_loader)\n",
+    "    print(f'Epoch {epoch:03d}, Loss: {loss:.4f}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
+   ]
   }
  ],
  "metadata": {
@@ -907,9 +1110,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap5",
    "language": "python",
-   "name": "python3"
+   "name": "chap5"
   },
   "language_info": {
    "codemirror_mode": {
@@ -921,9 +1124,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.8.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Chapter05/05_Planetoid.ipynb b/Chapter05/05_Planetoid.ipynb
new file mode 100644
index 0000000..1c82649
--- /dev/null
+++ b/Chapter05/05_Planetoid.ipynb
@@ -0,0 +1,715 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "azD4cTRtNMPD"
+   },
+   "source": [
+    "# Planetoid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z298uxJkikP9"
+   },
+   "source": [
+    "In Chapter 4 and 5 we have presented Planetoid.\n",
+    "\n",
+    "Planetoid is a framework designed for *semi-supervised learning* on graphs, particularly useful when only a small portion of the graph nodes are labeled. It is applied to problems where we have a graph-structured dataset, such as citation networks, social networks, or knowledge graphs.\n",
+    "\n",
+    "The key idea behind Planetoid is to learn node embeddings that are able to capture both graph structure and label information. Planetoid achieves this by combining a supervised loss (for labeled nodes) and an unsupervised loss (for the graph structure, inferred from the edges), leading to more accurate predictions for the unlabeled nodes. Specifically, it uses random walks to capture the local graph structure and applies a Skip-gram model (similar to Word2Vec) to learn node embeddings, while also incorporating label information to improve performance.\n",
+    "\n",
+    "Key Characteristics of Planetoid:\n",
+    "- Semi-supervised: Combines labeled and unlabeled data to improve the model.\n",
+    "- Graph-based: Uses graph structure information to learn the relationships between nodes.\n",
+    "- Random Walks & Skip-gram: Uses random walks on the graph to capture the local structure and applies a Skip-gram model to learn embeddings.\n",
+    "\n",
+    "Planetoid comes in two version, a _transductive_ and an _inductive_ one, namely *Planetoid-T* and *Planetoid-I*\n",
+    "\n",
+    "The original implementation can be found at [this repo](https://github.com/kimiyoung/planetoid). However, it uses quite old frameworks such as Lasagne and theano. For this reason, we have provided a sample implementation below.\n",
+    "\n",
+    "_Important Note: since this is a custom implementation, results may be different from the original paper. However, it should be close enugh to capture the theoretical concept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "-RT2rekt_uST"
+   },
+   "outputs": [],
+   "source": [
+    "# adapted from https://github.com/kimiyoung/planetoid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9iG24owekG3g"
+   },
+   "source": [
+    "#### Planetoid-T: A Transductive Approach to Semi-Supervised Learning\n",
+    "Planetoid-T (Transductive Planetoid) is an extension of the original Planetoid framework. While Planetoid is primarily inductive (learning embeddings that generalize to unseen nodes), Planetoid-T specifically focuses on a transductive setting, where all nodes (both labeled and unlabeled) are known during training, and the model uses this to learn embeddings for all nodes in the graph.\n",
+    "\n",
+    "In Planetoid-T, the model learns node embeddings in a way that explicitly incorporates both labeled information and graph structure by utilizing two types of losses:\n",
+    "\n",
+    "1. Supervised Loss: This is based on the labeled nodes, encouraging the model to predict the correct label for these nodes.\n",
+    "2. Unsupervised Loss: This loss captures the graph structure by predicting node pairs based on their proximity in the graph.\n",
+    "\n",
+    "The architecture used in Planetoid-T is typically a multi-layer feed-forward neural network where embeddings for nodes are refined during training. It combines the benefits of label propagation (through graph structure) and deep learning (through neural networks), making it an effective approach for large-scale semi-supervised learning tasks on graph-structured data.\n",
+    "\n",
+    "_importat note: The implementation below may require a lot of time. However its easier to follow from a didactic point of view. To speed up the process, we have provided a faster implementation using sparse tensors in the next cell_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "vSl1MoWzBCs7"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.optim import Adam\n",
+    "from torch_geometric.datasets import Planetoid\n",
+    "from torch_geometric.utils import to_dense_adj\n",
+    "from torch_geometric.utils import subgraph\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Load Cora dataset\n",
+    "dataset = Planetoid(root=\"data/Cora\", name=\"Cora\")\n",
+    "data = dataset[0]  # Graph object\n",
+    "\n",
+    "###########################################\n",
+    "# Computing embeddings and training may require a lot of time.\n",
+    "# You may want to subsample the graph for didactic purposes.\n",
+    "# Do it here.\n",
+    "###########################################\n",
+    "\n",
+    "features = data.x  # Node features\n",
+    "labels = data.y  # Node labels\n",
+    "adj_matrix = to_dense_adj(data.edge_index)[0]  # Dense adjacency matrix\n",
+    "num_nodes, feature_dim = features.shape\n",
+    "num_classes = dataset.num_classes\n",
+    "\n",
+    "# Parameters\n",
+    "hidden_dim = 64\n",
+    "embedding_dim = 64\n",
+    "learning_rate = 0.01\n",
+    "lambda_weight = 0.5  # Weight for unsupervised loss\n",
+    "pretrain_epochs = 100\n",
+    "train_epochs = 200\n",
+    "neg_sample_ratio = 1.0  # Ratio of negative samples to positive samples\n",
+    "random_walk_length = 10\n",
+    "window_size = 5\n",
+    "\n",
+    "# Random Walk Sampling\n",
+    "def random_walk_sampling(adj_matrix, num_walks=10, walk_length=10):\n",
+    "    \"\"\"Generates random walk sequences.\"\"\"\n",
+    "    walks = []\n",
+    "    for node in range(num_nodes):\n",
+    "        for _ in range(num_walks):\n",
+    "            walk = [node]\n",
+    "            for _ in range(walk_length - 1):\n",
+    "                neighbors = torch.nonzero(torch.Tensor(adj_matrix[node]), as_tuple=True)[0].tolist()\n",
+    "                if neighbors:\n",
+    "                    walk.append(np.random.choice(neighbors))\n",
+    "                else:\n",
+    "                    break\n",
+    "            walks.append(walk)\n",
+    "    return walks\n",
+    "\n",
+    "# Generate positive and negative pairs\n",
+    "def generate_pairs(walks, window_size, neg_sample_ratio):\n",
+    "    \"\"\"Generates positive and negative samples for context prediction.\"\"\"\n",
+    "    pairs = []\n",
+    "    for walk in tqdm(walks):\n",
+    "        for i, node in enumerate(walk):\n",
+    "            # Positive pairs within the sliding window\n",
+    "            for j in range(max(0, i - window_size), min(len(walk), i + window_size + 1)):\n",
+    "                if i != j:\n",
+    "                    pairs.append((node, walk[j], 1))  # Positive pair\n",
+    "            # Negative pairs (corrupted context)\n",
+    "            for _ in range(int(neg_sample_ratio)):\n",
+    "                neg_node = np.random.randint(0, num_nodes)\n",
+    "                pairs.append((node, neg_node, -1))\n",
+    "    return pairs\n",
+    "\n",
+    "# Model definition\n",
+    "class PlanetoidT(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim, embedding_dim):\n",
+    "        super(PlanetoidT, self).__init__()\n",
+    "        # Feature-based layers\n",
+    "        self.feature_nn = nn.Sequential(\n",
+    "            nn.Linear(input_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, output_dim),\n",
+    "        )\n",
+    "        # Embedding-based layers\n",
+    "        self.embedding_nn = nn.Sequential(\n",
+    "            nn.Linear(embedding_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, output_dim),\n",
+    "        )\n",
+    "        # Parameters for embeddings\n",
+    "        self.embeddings = nn.Parameter(torch.randn(num_nodes, embedding_dim))\n",
+    "        # Classifier\n",
+    "        self.classifier = nn.Linear(2 * output_dim, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        h_feature = self.feature_nn(x)\n",
+    "        h_embedding = self.embedding_nn(self.embeddings)\n",
+    "        combined = torch.cat([h_feature, h_embedding], dim=1)\n",
+    "        return self.classifier(combined)\n",
+    "\n",
+    "# Loss functions\n",
+    "def supervised_loss(predictions, labels, mask):\n",
+    "    \"\"\"Cross-entropy loss for labeled nodes.\"\"\"\n",
+    "    return F.cross_entropy(predictions[mask], labels[mask])\n",
+    "\n",
+    "def unsupervised_loss(pairs, embeddings):\n",
+    "    \"\"\"Negative sampling-based loss for graph context prediction.\"\"\"\n",
+    "    loss = 0\n",
+    "    for ith, (i, c, label) in enumerate(tqdm(pairs)):\n",
+    "        score = torch.dot(embeddings[i], embeddings[c])\n",
+    "        old_loss = float(loss)\n",
+    "        if label == 1:\n",
+    "            loss += -torch.log(torch.sigmoid(score))\n",
+    "        else:\n",
+    "            loss += -torch.log(1 - torch.sigmoid(score))\n",
+    "        if torch.isnan(loss) or torch.isinf(loss):\n",
+    "            print(loss)\n",
+    "            print(torch.log(torch.sigmoid(score)))\n",
+    "            print(old_loss)\n",
+    "            print(score)\n",
+    "            print(label)\n",
+    "            raise ValueError()\n",
+    "    return loss / len(pairs)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "id": "vSl1MoWzBCs7"
+   },
+   "source": [
+    "# Training setup\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "model = PlanetoidT(input_dim=feature_dim, hidden_dim=hidden_dim, output_dim=hidden_dim, embedding_dim=embedding_dim).to(device)\n",
+    "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
+    "\n",
+    "features, labels, adj_matrix = features.to(device), labels.to(device), adj_matrix.to(device)\n",
+    "train_mask = data.train_mask.to(device)\n",
+    "test_mask = data.test_mask.to(device)\n",
+    "\n",
+    "# Pretraining embeddings\n",
+    "print(\"Pretraining embeddings...\")\n",
+    "walks = random_walk_sampling(adj_matrix.cpu().numpy(), num_walks=10, walk_length=random_walk_length)\n",
+    "print(\"Generating pairs...\")\n",
+    "pairs = generate_pairs(walks, window_size=window_size, neg_sample_ratio=neg_sample_ratio)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "id": "vSl1MoWzBCs7"
+   },
+   "source": [
+    "for epoch in range(pretrain_epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    embeddings = model.embeddings\n",
+    "    loss = unsupervised_loss(pairs, embeddings)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    if epoch % 10 == 0:\n",
+    "        print(f\"Pretraining Epoch {epoch:03d}, Loss: {loss.item():.4f}\")\n",
+    "\n",
+    "# Joint training\n",
+    "print(\"Joint training...\")\n",
+    "for epoch in range(train_epochs):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # Supervised loss\n",
+    "    predictions = model(features)\n",
+    "    Ls = supervised_loss(predictions, labels, train_mask)\n",
+    "\n",
+    "    # Unsupervised loss\n",
+    "    embeddings = model.embeddings\n",
+    "    Lu = unsupervised_loss(pairs, embeddings)\n",
+    "\n",
+    "    # Combined loss\n",
+    "    loss = Ls + lambda_weight * Lu\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    if epoch % 10 == 0:\n",
+    "        print(f\"Epoch {epoch:03d}, Loss: {loss.item():.4f}, Ls: {Ls.item():.4f}, Lu: {Lu.item():.4f}\")\n",
+    "\n",
+    "# Evaluation\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    predictions = model(features).argmax(dim=1)\n",
+    "    accuracy = (predictions[test_mask] == labels[test_mask]).float().mean()\n",
+    "    print(f\"Test Accuracy: {accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iHKhTmZlk8yt"
+   },
+   "source": [
+    "##### Faster implementation of Planetoid-T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ww0a9xyYUDVC",
+    "outputId": "f641cb99-b519-4ccd-e647-ea6910df7aa7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pretraining embeddings...\n",
+      "Pretraining Epoch 000, Loss: 3.2761\n",
+      "Pretraining Epoch 010, Loss: 2.8495\n",
+      "Pretraining Epoch 020, Loss: 2.4809\n",
+      "Pretraining Epoch 030, Loss: 2.1658\n",
+      "Pretraining Epoch 040, Loss: 1.9020\n",
+      "Joint training...\n",
+      "Epoch 000, Loss: 2.7846, Ls: 1.9508, Lu: 1.6677\n",
+      "Epoch 010, Loss: 0.7608, Ls: 0.0014, Lu: 1.5189\n",
+      "Epoch 020, Loss: 0.7075, Ls: 0.0000, Lu: 1.4151\n",
+      "Epoch 030, Loss: 0.6647, Ls: 0.0000, Lu: 1.3294\n",
+      "Epoch 040, Loss: 0.6297, Ls: 0.0000, Lu: 1.2595\n",
+      "Epoch 050, Loss: 0.5959, Ls: 0.0000, Lu: 1.1917\n",
+      "Epoch 060, Loss: 0.5666, Ls: 0.0000, Lu: 1.1331\n",
+      "Epoch 070, Loss: 0.5413, Ls: 0.0000, Lu: 1.0826\n",
+      "Epoch 080, Loss: 0.5166, Ls: 0.0000, Lu: 1.0333\n",
+      "Epoch 090, Loss: 0.4965, Ls: 0.0000, Lu: 0.9931\n",
+      "Test Accuracy: 0.4490\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch_geometric.datasets import Planetoid\n",
+    "from torch_geometric.utils import to_dense_adj\n",
+    "from torch_sparse import SparseTensor\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load Cora dataset\n",
+    "dataset = Planetoid(root=\"data/Cora\", name=\"Cora\")\n",
+    "data = dataset[0]\n",
+    "features = data.x\n",
+    "labels = data.y\n",
+    "adj_matrix = to_dense_adj(data.edge_index)[0]\n",
+    "num_nodes, feature_dim = features.shape\n",
+    "num_classes = dataset.num_classes\n",
+    "\n",
+    "# Parameters\n",
+    "hidden_dim = 64\n",
+    "embedding_dim = 64\n",
+    "learning_rate = 0.01\n",
+    "lambda_weight = 0.5\n",
+    "pretrain_epochs = 50\n",
+    "train_epochs = 100\n",
+    "neg_sample_ratio = 1.0\n",
+    "\n",
+    "# Convert adjacency to sparse tensor\n",
+    "adj_sparse = SparseTensor.from_dense(adj_matrix)\n",
+    "\n",
+    "# Efficient random walk sampling\n",
+    "def efficient_sample_context(adj, num_samples=10, neg_ratio=1.0):\n",
+    "    \"\"\"Sample positive and negative context pairs efficiently.\"\"\"\n",
+    "    row, col, _ = adj.coo()  # Edge list\n",
+    "    num_edges = row.size(0)\n",
+    "\n",
+    "    # Sample positive pairs\n",
+    "    idx = torch.randint(0, num_edges, (num_samples,))\n",
+    "    pos_pairs = torch.stack((row[idx], col[idx]), dim=1)\n",
+    "\n",
+    "    # Sample negative pairs\n",
+    "    neg_pairs = []\n",
+    "    for _ in range(int(num_samples * neg_ratio)):\n",
+    "        neg_src = torch.randint(0, num_nodes, (num_samples,))\n",
+    "        neg_dst = torch.randint(0, num_nodes, (num_samples,))\n",
+    "        neg_pairs.append(torch.stack((neg_src, neg_dst), dim=1))\n",
+    "    neg_pairs = torch.cat(neg_pairs, dim=0)\n",
+    "\n",
+    "    # Combine and return\n",
+    "    labels = torch.cat([torch.ones(pos_pairs.size(0)), -torch.ones(neg_pairs.size(0))])\n",
+    "    return torch.cat([pos_pairs, neg_pairs], dim=0), labels\n",
+    "\n",
+    "# Model definition\n",
+    "class PlanetoidT(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim, embedding_dim):\n",
+    "        super(PlanetoidT, self).__init__()\n",
+    "        self.feature_nn = nn.Sequential(\n",
+    "            nn.Linear(input_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, output_dim),\n",
+    "        )\n",
+    "        self.embedding_nn = nn.Sequential(\n",
+    "            nn.Linear(embedding_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, output_dim),\n",
+    "        )\n",
+    "        self.embeddings = nn.Parameter(torch.randn(num_nodes, embedding_dim))\n",
+    "        self.classifier = nn.Linear(2 * output_dim, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        h_feature = self.feature_nn(x)\n",
+    "        h_embedding = self.embedding_nn(self.embeddings)\n",
+    "        combined = torch.cat([h_feature, h_embedding], dim=1)\n",
+    "        return self.classifier(combined)\n",
+    "\n",
+    "# Loss functions\n",
+    "def supervised_loss(predictions, labels, mask):\n",
+    "    return F.cross_entropy(predictions[mask], labels[mask])\n",
+    "\n",
+    "def unsupervised_loss(context_pairs, labels, embeddings):\n",
+    "    src, dst = context_pairs[:, 0], context_pairs[:, 1]\n",
+    "    scores = (embeddings[src] * embeddings[dst]).sum(dim=1)\n",
+    "    return F.binary_cross_entropy_with_logits(scores, (labels + 1) / 2)\n",
+    "\n",
+    "# Training setup\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "model = PlanetoidT(input_dim=feature_dim, hidden_dim=hidden_dim, output_dim=hidden_dim, embedding_dim=embedding_dim).to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
+    "\n",
+    "features, labels = features.to(device), labels.to(device)\n",
+    "train_mask = data.train_mask.to(device)\n",
+    "test_mask = data.test_mask.to(device)\n",
+    "\n",
+    "# Pretraining embeddings\n",
+    "print(\"Pretraining embeddings...\")\n",
+    "for epoch in range(pretrain_epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    context_pairs, labels_context = efficient_sample_context(adj_sparse, num_samples=1000, neg_ratio=neg_sample_ratio)\n",
+    "    context_pairs, labels_context = context_pairs.to(device), labels_context.to(device)\n",
+    "    loss = unsupervised_loss(context_pairs, labels_context, model.embeddings)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    if epoch % 10 == 0:\n",
+    "        print(f\"Pretraining Epoch {epoch:03d}, Loss: {loss.item():.4f}\")\n",
+    "\n",
+    "# Joint training\n",
+    "print(\"Joint training...\")\n",
+    "for epoch in range(train_epochs):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # Supervised loss\n",
+    "    predictions = model(features)\n",
+    "    Ls = supervised_loss(predictions, labels, train_mask)\n",
+    "\n",
+    "    # Unsupervised loss\n",
+    "    context_pairs, labels_context = efficient_sample_context(adj_sparse, num_samples=1000, neg_ratio=neg_sample_ratio)\n",
+    "    context_pairs, labels_context = context_pairs.to(device), labels_context.to(device)\n",
+    "    Lu = unsupervised_loss(context_pairs, labels_context, model.embeddings)\n",
+    "\n",
+    "    # Combined loss\n",
+    "    loss = Ls + lambda_weight * Lu\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    if epoch % 10 == 0:\n",
+    "        print(f\"Epoch {epoch:03d}, Loss: {loss.item():.4f}, Ls: {Ls.item():.4f}, Lu: {Lu.item():.4f}\")\n",
+    "\n",
+    "# Evaluation\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    predictions = model(features).argmax(dim=1)\n",
+    "    accuracy = (predictions[test_mask] == labels[test_mask]).float().mean()\n",
+    "    print(f\"Test Accuracy: {accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "OLIFm8ICflPZ",
+    "outputId": "68f1f6d3-e486-4f78-8330-060a88954901"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import colors\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "def visualize_embeddings(embeddings, labels, title=\"Embeddings Visualization\"):\n",
+    "    \"\"\"\n",
+    "    Visualize the embeddings in 2D using t-SNE.\n",
+    "    :param embeddings: The node embeddings (tensor of shape [num_nodes, embedding_dim]).\n",
+    "    :param labels: The ground truth labels for the nodes.\n",
+    "    :param title: Title for the plot.\n",
+    "    \"\"\"\n",
+    "    # Reduce embeddings to 2D\n",
+    "    tsne = TSNE(n_components=2, random_state=42, perplexity=30)\n",
+    "    reduced_embeddings = tsne.fit_transform(embeddings.cpu().detach().numpy())\n",
+    "\n",
+    "    plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "    norm = colors.Normalize(vmin=0, vmax=10)\n",
+    "\n",
+    "    for lab in np.unique(labels):\n",
+    "        idx = np.where(node_labels==lab)\n",
+    "\n",
+    "        #colormap possible values = viridis, jet, spectral\n",
+    "        color = colors.to_hex(cm.Set2(norm(lab)))\n",
+    "        plt.scatter(\n",
+    "            x=reduced_embeddings[idx, 0],\n",
+    "            y=reduced_embeddings[idx, 1],\n",
+    "            color=color,\n",
+    "            label=lab,\n",
+    "            alpha=0.7\n",
+    "        )\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"t-SNE Dimension 1\")\n",
+    "    plt.ylabel(\"t-SNE Dimension 2\")\n",
+    "    plt.legend() #title=\"Class\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    plt.show()\n",
+    "\n",
+    "# Extract embeddings and labels\n",
+    "node_embeddings = model.embeddings\n",
+    "node_labels = labels\n",
+    "\n",
+    "# Visualize embeddings\n",
+    "visualize_embeddings(node_embeddings, node_labels, title=\"Cora Embeddings Visualization (Planetoid-T)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "501kV9zmlDDB"
+   },
+   "source": [
+    "As expected, results differs from the original implementation and more hyperparameter tuning may be needed to achive better results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xE3a2H-elVp0"
+   },
+   "source": [
+    "#### Planetoid-I: Inductive Learning for Semi-Supervised Graph Learning\n",
+    "While the transductive Planetoid framework is highly effective for scenarios where all nodes, including unlabeled ones, are observed during training, there are situations where we need a model that can generalize to unseen instances. This is especially important in large-scale or dynamic settings where new nodes (e.g., new entities, articles, or documents) continuously emerge, and retraining on the entire graph or dataset becomes impractical.\n",
+    "\n",
+    "Planetoid-I (Inductive Planetoid) addresses this challenge by introducing an inductive learning approach for graph embedding and semi-supervised learning. Unlike the transductive setting, where embeddings are learned for all nodes (labeled and unlabeled) simultaneously, Planetoid-I ensures that the learned model can be applied to unseen nodes during inference. This is achieved by designing the embeddings as a parameterized function of the input feature vector.\n",
+    "\n",
+    "###### Key Features of Planetoid-I:\n",
+    "- **Inductive Nature**: Embeddings are learned as functions of node features, allowing the model to generalize to new, unseen nodes.\n",
+    "- **Embedding as a Function of Input Features**: Instead of learning a fixed embedding for each node, the embedding is defined as a parameterized function of the node's input feature vector \\( x \\), making the approach inductive.\n",
+    "- **No Shared Embeddings**: Unlike the transductive approach, Planetoid-I does not use shared embeddings across all nodes but instead computes node embeddings dynamically based on features.\n",
+    "\n",
+    "###### Loss Function:\n",
+    "The **Planetoid-I** loss function combines two terms:\n",
+    "1. **Supervised Loss**: The first term encourages correct label prediction for labeled nodes.\n",
+    "   \\[\n",
+    "   L_s = -\\frac{1}{L} \\sum_{i=1}^{L} \\log p(y_i | x_i)\n",
+    "   \\]\n",
+    "2. **Unsupervised Loss**: The second term uses negative sampling to learn graph structure by predicting the context of nodes.\n",
+    "   \\[\n",
+    "   L_u = -\\lambda \\mathbb{E}_{(i, c, \\gamma)} \\log \\sigma(\\gamma w_c^T h_{l1}(x_i))\n",
+    "   \\]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "br_ecqVambgh",
+    "outputId": "5b792f7c-6329-4c7e-dd61-410e8d65fffe"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 176.1518, Test Accuracy: 0.3170\n",
+      "Epoch 11, Loss: 0.0145, Test Accuracy: 0.5070\n",
+      "Epoch 21, Loss: 0.0002, Test Accuracy: 0.4400\n",
+      "Epoch 31, Loss: 0.0001, Test Accuracy: 0.4200\n",
+      "Epoch 41, Loss: 0.0001, Test Accuracy: 0.4460\n",
+      "Epoch 51, Loss: 0.0002, Test Accuracy: 0.5030\n",
+      "Epoch 61, Loss: 0.0009, Test Accuracy: 0.5580\n",
+      "Epoch 71, Loss: 0.0018, Test Accuracy: 0.5710\n",
+      "Epoch 81, Loss: 0.0020, Test Accuracy: 0.5700\n",
+      "Epoch 91, Loss: 0.0021, Test Accuracy: 0.5730\n",
+      "Epoch 101, Loss: 0.0021, Test Accuracy: 0.5710\n",
+      "Epoch 111, Loss: 0.0020, Test Accuracy: 0.5730\n",
+      "Epoch 121, Loss: 0.0019, Test Accuracy: 0.5760\n",
+      "Epoch 131, Loss: 0.0019, Test Accuracy: 0.5750\n",
+      "Epoch 141, Loss: 0.0019, Test Accuracy: 0.5680\n",
+      "Epoch 151, Loss: 0.0019, Test Accuracy: 0.5680\n",
+      "Epoch 161, Loss: 0.0018, Test Accuracy: 0.5700\n",
+      "Epoch 171, Loss: 0.0019, Test Accuracy: 0.5730\n",
+      "Epoch 181, Loss: 0.0019, Test Accuracy: 0.5720\n",
+      "Epoch 191, Loss: 0.0019, Test Accuracy: 0.5710\n",
+      "Final Test Accuracy: 0.5710\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from sklearn.preprocessing import normalize\n",
+    "from sklearn.metrics import classification_report\n",
+    "from torch_geometric.datasets import Planetoid\n",
+    "from torch_geometric.utils import to_networkx\n",
+    "\n",
+    "# Load Cora dataset\n",
+    "dataset = Planetoid(root=\"data/Cora\", name=\"Cora\")\n",
+    "data = dataset[0]\n",
+    "\n",
+    "# Planetoid-I Model\n",
+    "class PlanetoidIModel(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim1, hidden_dim2, output_dim):\n",
+    "        super(PlanetoidIModel, self).__init__()\n",
+    "        self.fc1 = nn.Linear(input_dim, hidden_dim1)\n",
+    "        self.fc2 = nn.Linear(hidden_dim1, hidden_dim2)\n",
+    "        self.fc3 = nn.Linear(hidden_dim2, output_dim)\n",
+    "\n",
+    "        # This layer is the inductive part that computes the embedding directly from the features\n",
+    "        self.fc4 = nn.Linear(input_dim, hidden_dim1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inductive embeddings as a function of input features\n",
+    "        e = F.relu(self.fc4(x))  # Inductive embedding\n",
+    "        h = F.relu(self.fc1(x))  # First layer\n",
+    "        h = F.relu(self.fc2(h))  # Second layer\n",
+    "        h = self.fc3(h)          # Output layer\n",
+    "\n",
+    "        return h, e\n",
+    "\n",
+    "# Training function for Planetoid-I\n",
+    "def train_model(model, data, optimizer, criterion, lambda_):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # Forward pass\n",
+    "    output, e = model(data.x)\n",
+    "\n",
+    "    # Supervised loss (cross-entropy)\n",
+    "    supervised_loss = F.cross_entropy(output[data.train_mask], data.y[data.train_mask])\n",
+    "\n",
+    "    # Unsupervised loss (negative sampling)\n",
+    "    context_loss = 0\n",
+    "    negative_samples = 10  # Number of negative samples for each context\n",
+    "\n",
+    "    # Negative sampling\n",
+    "    for i in range(len(data.x)):\n",
+    "        pos_idx = i\n",
+    "        neg_idx = np.random.randint(0, len(data.x), negative_samples)\n",
+    "\n",
+    "        # Negative sampling loss calculation\n",
+    "        context_loss += -torch.log(torch.sigmoid(torch.matmul(e[pos_idx], e[neg_idx].T))).mean()\n",
+    "\n",
+    "    # Total loss\n",
+    "    loss = supervised_loss + lambda_ * context_loss\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    return loss.item()\n",
+    "\n",
+    "# Evaluate function for Planetoid-I\n",
+    "def evaluate_model(model, data):\n",
+    "    model.eval()\n",
+    "    output, _ = model(data.x)\n",
+    "    pred = output.argmax(dim=1)\n",
+    "    correct = (pred[data.test_mask] == data.y[data.test_mask]).sum().item()\n",
+    "    acc = correct / data.test_mask.sum().item()\n",
+    "    return acc\n",
+    "\n",
+    "# Hyperparameters\n",
+    "input_dim = dataset.num_features\n",
+    "hidden_dim1 = 128\n",
+    "hidden_dim2 = 64\n",
+    "output_dim = dataset.num_classes\n",
+    "lambda_ = 0.1  # Weight for unsupervised loss\n",
+    "learning_rate = 0.01\n",
+    "epochs = 200\n",
+    "\n",
+    "# Model, optimizer, and loss function\n",
+    "model = PlanetoidIModel(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
+    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=5e-4)\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(epochs):\n",
+    "    loss = train_model(model, data, optimizer, F.cross_entropy, lambda_)\n",
+    "    if epoch % 10 == 0:\n",
+    "        acc = evaluate_model(model, data)\n",
+    "        print(f'Epoch {epoch+1}, Loss: {loss:.4f}, Test Accuracy: {acc:.4f}')\n",
+    "\n",
+    "# Final Evaluation\n",
+    "acc = evaluate_model(model, data)\n",
+    "print(f'Final Test Accuracy: {acc:.4f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "chap5",
+   "language": "python",
+   "name": "chap5"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter05/poetry.lock b/Chapter05/poetry.lock
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index 0000000..fd26512
--- /dev/null
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+    {file = "yarl-1.14.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8f4e475f29a9122f908d0f1f706e1f2fc3656536ffd21014ff8a6f2e1b14d1d8"},
+    {file = "yarl-1.14.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8089d4634d8fa2b1806ce44fefa4979b1ab2c12c0bc7ef3dfa45c8a374811348"},
+    {file = "yarl-1.14.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1b16f6c75cffc2dc0616ea295abb0e1967601bd1fb1e0af6a1de1c6c887f3439"},
+    {file = "yarl-1.14.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:498b3c55087b9d762636bca9b45f60d37e51d24341786dc01b81253f9552a607"},
+    {file = "yarl-1.14.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e3f8bfc1db82589ef965ed234b87de30d140db8b6dc50ada9e33951ccd8ec07a"},
+    {file = "yarl-1.14.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:625f207b1799e95e7c823f42f473c1e9dbfb6192bd56bba8695656d92be4535f"},
+    {file = "yarl-1.14.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:781e2495e408a81e4eaeedeb41ba32b63b1980dddf8b60dbbeff6036bcd35049"},
+    {file = "yarl-1.14.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:659603d26d40dd4463200df9bfbc339fbfaed3fe32e5c432fe1dc2b5d4aa94b4"},
+    {file = "yarl-1.14.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:4e0d45ebf975634468682c8bec021618b3ad52c37619e5c938f8f831fa1ac5c0"},
+    {file = "yarl-1.14.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:a2e4725a08cb2b4794db09e350c86dee18202bb8286527210e13a1514dc9a59a"},
+    {file = "yarl-1.14.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:19268b4fec1d7760134f2de46ef2608c2920134fb1fa61e451f679e41356dc55"},
+    {file = "yarl-1.14.0-cp39-cp39-win32.whl", hash = "sha256:337912bcdcf193ade64b9aae5a4017a0a1950caf8ca140362e361543c6773f21"},
+    {file = "yarl-1.14.0-cp39-cp39-win_amd64.whl", hash = "sha256:b6d0147574ce2e7b812c989e50fa72bbc5338045411a836bd066ce5fc8ac0bce"},
+    {file = "yarl-1.14.0-py3-none-any.whl", hash = "sha256:c8ed4034f0765f8861620c1f2f2364d2e58520ea288497084dae880424fc0d9f"},
+    {file = "yarl-1.14.0.tar.gz", hash = "sha256:88c7d9d58aab0724b979ab5617330acb1c7030b79379c8138c1c8c94e121d1b3"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "89f2bed3549c14904b7131c0b405e7f7ad775c03b09f33e2364d23ec8d456645"
diff --git a/Chapter05/pyproject.toml b/Chapter05/pyproject.toml
new file mode 100644
index 0000000..6884208
--- /dev/null
+++ b/Chapter05/pyproject.toml
@@ -0,0 +1,36 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 5"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+numpy = "==1.21.6"
+neural-structured-learning = "==1.3.1"
+networkx = "==2.5"
+tensorflow = "^2.6.0" 
+tensorflow-io-gcs-filesystem = "==0.21.0" # This needs pinning - see https://stackoverflow.com/a/76477590 
+stellargraph= "^1.2.1"
+protobuf= "^3.20"
+torch = "^2.1.0"
+chardet = "==5.2.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+torch-scatter = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+# Since 2024.06.27, DGL have stopped providing packages for Windows and MacOS. The latest version of available package is 2.2.1.
+dgl = {url = "https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl"}
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
diff --git a/Chapter05/requirements.txt b/Chapter05/requirements.txt
new file mode 100644
index 0000000..68c0395
--- /dev/null
+++ b/Chapter05/requirements.txt
@@ -0,0 +1,134 @@
+absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+aiohappyeyeballs==2.4.3 ; python_version >= "3.8" and python_version < "3.9"
+aiohttp==3.10.10 ; python_version >= "3.8" and python_version < "3.9"
+aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+annotated-types==0.7.0 ; python_version >= "3.8" and python_version < "3.9"
+appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9"
+async-timeout==4.0.3 ; python_version >= "3.8" and python_version < "3.9"
+attrs==24.2.0 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+cachetools==5.5.0 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+chardet==5.2.0 ; python_version >= "3.8" and python_version < "3.9"
+charset-normalizer==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.7 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+dgl @ https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+filelock==3.16.1 ; python_version >= "3.8" and python_version < "3.9"
+flatbuffers==2.0.7 ; python_version >= "3.8" and python_version < "3.9"
+frozenlist==1.4.1 ; python_version >= "3.8" and python_version < "3.9"
+fsspec==2024.9.0 ; python_version >= "3.8" and python_version < "3.9"
+gast==0.4.0 ; python_version >= "3.8" and python_version < "3.9"
+gensim==4.3.3 ; python_version >= "3.8" and python_version < "3.9"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.9"
+google-auth==2.35.0 ; python_version >= "3.8" and python_version < "3.9"
+google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+grpcio==1.66.2 ; python_version >= "3.8" and python_version < "3.9"
+h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.10 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.1 ; python_version >= "3.8" and python_version < "3.9"
+jinja2==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9"
+keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9"
+keras==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9"
+libclang==18.1.1 ; python_version >= "3.8" and python_version < "3.9"
+lightning-utilities==0.11.7 ; python_version >= "3.8" and python_version < "3.9"
+markdown==3.7 ; python_version >= "3.8" and python_version < "3.9"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9"
+multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9"
+networkx==2.5 ; python_version >= "3.8" and python_version < "3.9"
+neural-structured-learning==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.21.6 ; python_version >= "3.8" and python_version < "3.9"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+packaging==24.1 ; python_version >= "3.8" and python_version < "3.9"
+pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9"
+propcache==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9"
+psutil==6.0.0 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pydantic-core==2.23.4 ; python_version >= "3.8" and python_version < "3.9"
+pydantic==2.9.2 ; python_version >= "3.8" and python_version < "3.9"
+pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==307 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyyaml==6.0.2 ; python_version >= "3.8" and python_version < "3.9"
+pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9"
+requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9"
+rsa==4.9 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==1.3.2 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+setuptools==75.1.0 ; python_version >= "3.8" and python_version < "3.9"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==7.0.5 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9"
+sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9"
+termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9"
+torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9"
+torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9"
+torchmetrics==1.4.3 ; python_version >= "3.8" and python_version < "3.9"
+torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.66.5 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9"
+tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9"
+werkzeug==3.0.4 ; python_version >= "3.8" and python_version < "3.9"
+wheel==0.44.0 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+yarl==1.14.0 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter06/01_link_prediction.ipynb b/Chapter06/01_link_prediction.ipynb
new file mode 100644
index 0000000..07d75a1
--- /dev/null
+++ b/Chapter06/01_link_prediction.ipynb
@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Similarity Based Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Index Based"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")                \n",
+    "from utils import draw_graph, DATA_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resource Allocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 2, 0.5), (2, 5, 0.5), (3, 4, 0.5)]\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
+    "G = nx.from_edgelist(edges)\n",
+    "preds = nx.resource_allocation_index(G,[(1,2),(2,5),(3,4)])\n",
+    "print(list(preds))\n",
+    "draw_graph(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Jaccard Coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 2, 0.5), (2, 5, 0.25), (3, 4, 0.3333333333333333)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
+    "G = nx.from_edgelist(edges)\n",
+    "preds = nx.jaccard_coefficient(G,[(1,2),(2,5),(3,4)])\n",
+    "print(list(preds))\n",
+    "draw_graph(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Community Based"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Community Common Neighbor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 2, 2), (2, 5, 1), (3, 4, 1)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
+    "G = nx.from_edgelist(edges)\n",
+    "\n",
+    "G.nodes[1][\"community\"] = 0\n",
+    "G.nodes[2][\"community\"] = 0\n",
+    "G.nodes[3][\"community\"] = 0\n",
+    "\n",
+    "G.nodes[4][\"community\"] = 1\n",
+    "G.nodes[5][\"community\"] = 1\n",
+    "G.nodes[6][\"community\"] = 1\n",
+    "G.nodes[7][\"community\"] = 1\n",
+    "preds = nx.cn_soundarajan_hopcroft(G,[(1,2),(2,5),(3,4)])\n",
+    "print(list(preds))\n",
+    "draw_graph(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Community Common Neighbor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 2, 0.5), (2, 5, 0), (3, 4, 0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "edges = [[1,3],[2,3],[2,4],[4,5],[5,6],[5,7]]\n",
+    "G = nx.from_edgelist(edges)\n",
+    "\n",
+    "G.nodes[1][\"community\"] = 0\n",
+    "G.nodes[2][\"community\"] = 0\n",
+    "G.nodes[3][\"community\"] = 0\n",
+    "\n",
+    "G.nodes[4][\"community\"] = 1\n",
+    "G.nodes[5][\"community\"] = 1\n",
+    "G.nodes[6][\"community\"] = 1\n",
+    "G.nodes[7][\"community\"] = 1\n",
+    "preds = nx.ra_index_soundarajan_hopcroft(G,[(1,2),(2,5),(3,4)])\n",
+    "print(list(preds))\n",
+    "draw_graph(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embedding based"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from zipfile import ZipFile\n",
+    "from io import BytesIO\n",
+    "import shutil\n",
+    "import tarfile\n",
+    "\n",
+    "url = 'https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz'\n",
+    "\n",
+    "tmp_file = DATA_DIR / os.path.basename(url)\n",
+    "\n",
+    "with open(tmp_file, \"wb\") as fid:\n",
+    "    r = requests.get(url, allow_redirects=True)\n",
+    "    fid.write(r.content)\n",
+    "\n",
+    "with tarfile.open(tmp_file, \"r:gz\") as tar:\n",
+    "    tar.extractall(DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "\n",
+    "edgelist = pd.read_csv(os.path.join(DATA_DIR, \"cora\", \"cora.cites\"), sep='\\t', header=None, names=[\"target\", \"source\"])\n",
+    "G = nx.from_pandas_edgelist(edgelist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-16 09:27:09.398363: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2024-08-16 09:27:09.398381: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** Sampled 527 positive and 527 negative edges. **\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-16 09:27:15.201749: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2024-08-16 09:27:15.201776: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2024-08-16 09:27:15.201791: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (pelican): /proc/driver/nvidia/version does not exist\n",
+      "2024-08-16 09:27:15.202117: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from stellargraph.data import EdgeSplitter\n",
+    "\n",
+    "edgeSplitter = EdgeSplitter(G)\n",
+    "graph_test, samples_test, labels_test = edgeSplitter.train_test_split(\n",
+    "    p=0.1, method=\"global\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** Sampled 475 positive and 475 negative edges. **\n"
+     ]
+    }
+   ],
+   "source": [
+    "edgeSplitter = EdgeSplitter(graph_test, G)\n",
+    "graph_train, samples_train, labels_train = edgeSplitter.train_test_split(\n",
+    "    p=0.1, method=\"global\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|█████████████████████████| 2708/2708 [00:00<00:00, 12569.61it/s]\n",
+      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████| 10/10 [01:10<00:00,  7.04s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from node2vec import Node2Vec\n",
+    "from node2vec.edges import HadamardEmbedder\n",
+    "\n",
+    "node2vec = Node2Vec(graph_train)\n",
+    "model = node2vec.fit()\n",
+    "edges_embs = HadamardEmbedder(keyed_vectors=model.wv)\n",
+    "train_embeddings = [edges_embs[str(x[0]),str(x[1])] for x in samples_train]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_embeddings = [edges_embs[str(x[0]),str(x[1])] for x in samples_test]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "rf = RandomForestClassifier(n_estimators=1000)\n",
+    "rf.fit(train_embeddings, labels_train);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision: 0.8640167364016736\n",
+      "Recall: 0.7836812144212524\n",
+      "F1-Score: 0.8218905472636816\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "\n",
+    "y_pred = rf.predict(test_embeddings)\n",
+    "\n",
+    "print('Precision:', metrics.precision_score(labels_test, y_pred))\n",
+    "print('Recall:', metrics.recall_score(labels_test, y_pred))\n",
+    "print('F1-Score:', metrics.f1_score(labels_test, y_pred))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap6",
+   "language": "python",
+   "name": "chap6"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter06/02_community_detection_algorithms.ipynb b/Chapter06/02_community_detection_algorithms.ipynb
new file mode 100644
index 0000000..8dc2767
--- /dev/null
+++ b/Chapter06/02_community_detection_algorithms.ipynb
@@ -0,0 +1,585 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Network Communities Detection "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will explore some methods to perform a community detection using several algortihms. Before testing the algorithms, let us create a simple benchmark graph. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx \n",
+    "G = nx.barbell_graph(m1=10, m2=4) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Matrix Factorization "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by using some matrix factorization technique to extract the embeddings, which are visualized and then clustered traditional clustering algorithms.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVD error (low rank): 0.052092\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gem.embedding.hope import HOPE \n",
+    "gf = HOPE(d=4, beta=0.01) \n",
+    "gf.learn_embedding(G) \n",
+    "embeddings = gf.get_embedding() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsne = TSNE(n_components=2) \n",
+    "\n",
+    "emb2d = tsne.fit_transform(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x719dab5aefa0>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(embeddings[:, 0], embeddings[:, 1], 'o', linewidth=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by using a GaussianMixture model to perform the clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.mixture import GaussianMixture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gm = GaussianMixture(n_components=3, random_state=0) #.(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = gm.fit_predict(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = [\"blue\", \"green\", \"red\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nx.draw_spring(G, node_color=[colors[label] for label in labels])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spectral Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now perform a spectral clustering based on the adjacency matrix of the graph. It is worth noting that this clustering is not a mutually exclusive clustering and nodes may belong to more than one community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adj=np.array(nx.adjacency_matrix(G).todense())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from communities.algorithms import spectral_clustering\n",
+    "\n",
+    "communities = spectral_clustering(adj, k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next plot we highlight the nodes that belong to a community using the red color. The blue nodes do not belong to the given community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "\n",
+    "for ith, community in enumerate(communities):\n",
+    "    cols = [\"red\" if node in community else \"blue\" for node in G.nodes]\n",
+    "    plt.subplot(1,3,ith+1)\n",
+    "    plt.title(f\"Community {ith}\")\n",
+    "    nx.draw_spring(G, node_color=cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next command shows the node ids belonging to the different communities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{15, 16, 17, 18, 19, 20, 21, 22, 23},\n",
+       " {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13},\n",
+       " {13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Non Negative Matrix Factorization "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we again use matrix factorization, but now using the Non-Negative Matrix Factorization, and associating the clusters with the latent dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import NMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nmf = NMF(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py:312: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n",
+      "  warnings.warn((\"The 'init' value, when 'init=None' and \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "emb = nmf.fit_transform(adj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x719de072a280>]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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NmzdPNTU1Sk9P7/N+5eXlCgaD0Udra+sgTgkAAEwaYWdxenq6HA6HAoFAt+2BQECZmZlnrP/888914MABFRUVRbdFIpFTTzxihPbs2aMJEyacsZ/T6ZTT6bQzGgAAiFO2zoykpKQoNzdXdXV10W2RSER1dXUqKCg4Y/1ll12mzz77TM3NzdHHrbfeqhtuuEHNzc1cfgEAAPbOjEhSWVmZ5s+fr+nTpysvL0/r169XR0eHSkpKJEnFxcXKzs5WZWWlXC6XJk+e3G3/0aNHS9IZ2wEAQGKyHSNz5szRkSNHtHz5cvn9fk2dOlW1tbXRm1oPHjyo5GS+2BUAAPRNkmVZlukhziUUCsntdisYDCotLc30OAAAoA/6+v7NKQwAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFH9ipGqqirl5OTI5XIpPz9fDQ0Nva6tqanRzJkzNWbMGI0ZM0Y+n++s6wEAQGKxHSObNm1SWVmZKioq1NTUpClTpqiwsFCHDx/ucf22bdt0++23669//avq6+vl9Xp144036osvvjjv4QEAQPxLsizLsrNDfn6+rr76am3YsEGSFIlE5PV69cADD2jx4sXn3D8cDmvMmDHasGGDiouL+/ScoVBIbrdbwWBQaWlpdsYFAACG9PX929aZka6uLjU2Nsrn833zA5KT5fP5VF9f36efcezYMZ04cUJjx46189QAAGCYGmFncVtbm8LhsDweT7ftHo9Hu3fv7tPPWLRokcaNG9ctaL6rs7NTnZ2d0T+HQiE7YwIAgDgS00/TrFmzRhs3btTbb78tl8vV67rKykq53e7ow+v1xnBKAAAQS7ZiJD09XQ6HQ4FAoNv2QCCgzMzMs+77xBNPaM2aNfrggw905ZVXnnVteXm5gsFg9NHa2mpnTAAAEEdsxUhKSopyc3NVV1cX3RaJRFRXV6eCgoJe91u7dq1WrVql2tpaTZ8+/ZzP43Q6lZaW1u0BAACGJ1v3jEhSWVmZ5s+fr+nTpysvL0/r169XR0eHSkpKJEnFxcXKzs5WZWWlJOmxxx7T8uXL9dprryknJ0d+v1+SdOGFF+rCCy8cwJcCAADike0YmTNnjo4cOaLly5fL7/dr6tSpqq2tjd7UevDgQSUnf3PC5dlnn1VXV5d+8YtfdPs5FRUVWrFixflNDwAA4p7t7xkxge8ZAQAg/gzK94wAAAAMNGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFEjTA9gSjhiqaHlqA63H1dGqkt548fKkZxkeiwAABJOQsZI7Y5DWvnuTh0KHo9uy3K7VFE0STdNzjI4GQAAiSfhLtPU7jikBa80dQsRSfIHj2vBK02q3XHI0GQAACSmhIqRcMTSynd3yurh705vW/nuToUjPa0AAACDIaFipKHl6BlnRL7NknQoeFwNLUdjNxQAAAkuoWLkcHvvIdKfdQAA4PwlVIxkpLoGdB0AADh/CRUjeePHKsvtUm8f4E3SqU/V5I0fG8uxAABIaAkVI47kJFUUTZKkM4Lk9J8riibxfSMAAMRQQsWIJN00OUvP/vIqZbq7X4rJdLv07C+v4ntGAACIsYT80rObJmfpZ5My+QZWAACGgISMEenUJZuCCf9jegwAABJewl2mAQAAQwsxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGjTA9gEnhiKWGlqM63H5cGaku5Y0fK0dykumxAABIKAkbI7U7Dmnluzt1KHg8ui3L7VJF0STdNDnL4GQAACSWhLxMU7vjkBa80tQtRCTJHzyuBa80qXbHIUOTAQCQeBIuRsIRSyvf3Smrh787vW3luzsVjvS0AgAADLSEu0zT0HL0jDMi32ZJOhQ8rj9s3aMZl3w/Lu4j4d4XAMC5fHH0a9381N/U0RnW95wO/fk31ylztGtIvH/0K0aqqqr0+OOPy+/3a8qUKXr66aeVl5fX6/o33nhDy5Yt04EDB/SjH/1Ijz32mGbNmtXvoc/H4fbeQ+TbNvz1c2346+dD/j4S7n0BAJzLxCVb1BX+5ox/6HhYM9Z+eMY6U+8fti/TbNq0SWVlZaqoqFBTU5OmTJmiwsJCHT58uMf1H3/8sW6//Xbdeeed+vTTTzV79mzNnj1bO3bsOO/h+yMj1WVr/VC+j4R7XwAA5/LdEDkbU+8fSZZl2bo5Ij8/X1dffbU2bNggSYpEIvJ6vXrggQe0ePHiM9bPmTNHHR0deu+996LbfvKTn2jq1Kmqrq7u03OGQiG53W4Fg0GlpaXZGfcM4Yilax/7UP7g8R7vG+lJkqRMt0vbF/10yFz+OP06ervkNBRnBgDE1hdHv+7xDMjZDOT7R1/fv22dGenq6lJjY6N8Pt83PyA5WT6fT/X19T3uU19f3229JBUWFva6XpI6OzsVCoW6PQaKIzlJFUWTJJ36D94Xp+8jaWg5OmBznK++3vsylGYGAMTWzU/9zfY+Jt4/bMVIW1ubwuGwPB5Pt+0ej0d+v7/Hffx+v631klRZWSm32x19eL1eO2Oe002Ts/TsL69SptveJZu+3m8SC32dZSjNDACIrY7OcL/3jeX7x5D8aG95ebmCwWD00draOuDPcdPkLG1f9FP98e6f6P4bLunTPnbvNxlMfZ1lKM0MAIit7zkd/d43lu8ftmIkPT1dDodDgUCg2/ZAIKDMzMwe98nMzLS1XpKcTqfS0tK6PQaDIzlJBRP+Rwt/NlFZblevl22SdOoO47zxYwdljv7IGz827mYGAMTWn39zne19TLx/2IqRlJQU5ebmqq6uLrotEomorq5OBQUFPe5TUFDQbb0kbd26tdf1JpztPpLTf64omjSkbgSNx5kBALGVPXaUUhx9fx8w9f5h+zJNWVmZampq9PLLL2vXrl1asGCBOjo6VFJSIkkqLi5WeXl5dP2DDz6o2tparVu3Trt379aKFSv0ySef6P777x+4VzEAeruPJNPt0rO/vGpIfmdHPM4MAIitvY/O6nOQmHr/sP3RXknasGFD9EvPpk6dqqeeekr5+fmSpOuvv145OTl66aWXouvfeOMNLV26NPqlZ2vXrrX1pWcD+dHec4nHbzONx5kBALFl4htY+/r+3a8YibVYxggAABgYg/I9IwAAAAONGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADBqhOkB+uL0l8SGQiHDkwAAgL46/b59ri97j4sYaW9vlyR5vV7DkwAAALva29vldrt7/fu4+N00kUhEX375pVJTU5WUNLC/wMfr9aq1tZXfeRMHOF7xh2MWXzhe8SUejpdlWWpvb9e4ceOUnNz7nSFxcWYkOTlZF1100aD9/LS0tCF7IHEmjlf84ZjFF45XfBnqx+tsZ0RO4wZWAABgFDECAACMcqxYsWKF6SFMcjgcuv766zViRFxcsUp4HK/4wzGLLxyv+DJcjldc3MAKAACGLy7TAAAAo4gRAABgFDECAACMIkYAAIBRwz5GqqqqlJOTI5fLpfz8fDU0NJx1/RtvvKHLLrtMLpdLV1xxhbZs2RKjSSHZO141NTWaOXOmxowZozFjxsjn853z+GJg2f33ddrGjRuVlJSk2bNnD/KE+C67x+yrr75SaWmpsrKy5HQ6NXHiRP6/GEN2j9f69et16aWXatSoUfJ6vVq4cKGOHz8eo2nPgzWMbdy40UpJSbFefPFF65///Kd19913W6NHj7YCgUCP6z/66CPL4XBYa9eutXbu3GktXbrUGjlypPXZZ5/FePLEZPd4zZ0716qqqrI+/fRTa9euXdavfvUry+12W//+979jPHlisnu8TmtpabGys7OtmTNnWj//+c9jNC0sy/4x6+zstKZPn27NmjXL2r59u9XS0mJt27bNam5ujvHkicnu8Xr11Vctp9Npvfrqq1ZLS4v1/vvvW1lZWdbChQtjPLl9wzpG8vLyrNLS0uifw+GwNW7cOKuysrLH9bfddpt1yy23dNuWn59v/frXvx7UOXGK3eP1XSdPnrRSU1Otl19+ebBGxLf053idPHnSuuaaa6znn3/emj9/PjESY3aP2bPPPmtdfPHFVldXV6xGxLfYPV6lpaXWT3/6027bysrKrBkzZgzqnANh2F6m6erqUmNjo3w+X3RbcnKyfD6f6uvre9ynvr6+23pJKiws7HU9Bk5/jtd3HTt2TCdOnNDYsWMHa0z8n/4er4cfflgZGRm68847YzEmvqU/x+ydd95RQUGBSktL5fF4NHnyZK1evVrhcDhWYyes/hyva665Ro2NjdFLOfv379eWLVs0a9asmMx8PuL7K9vOoq2tTeFwWB6Pp9t2j8ej3bt397iP3+/vcb3f7x+0OXFKf47Xdy1atEjjxo07Iygx8PpzvLZv364XXnhBzc3NsRgR39GfY7Z//359+OGHuuOOO7Rlyxbt27dP9913n06cOKGKiopYjJ2w+nO85s6dq7a2Nl177bWyLEsnT57Uvffeq4ceeigWI5+XYXtmBIllzZo12rhxo95++225XC7T4+A72tvbNW/ePNXU1Cg9Pd30OOijSCSijIwMPffcc8rNzdWcOXO0ZMkSVVdXmx4NPdi2bZtWr16tZ555Rk1NTXrrrbe0efNmrVq1yvRo5zRsz4ykp6fL4XAoEAh02x4IBJSZmdnjPpmZmbbWY+D053id9sQTT2jNmjX6y1/+oiuvvHIwx8T/sXu8Pv/8cx04cEBFRUXRbZFIRJI0YsQI7dmzRxMmTBjcoRNcf/6NZWVlaeTIkXI4HNFtl19+ufx+v7q6upSSkjKoMyey/hyvZcuWad68ebrrrrskSVdccYU6Ojp0zz33aMmSJUpOHrrnH4buZOcpJSVFubm5qquri26LRCKqq6tTQUFBj/sUFBR0Wy9JW7du7XU9Bk5/jpckrV27VqtWrVJtba2mT58ei1Eh+8frsssu02effabm5ubo49Zbb9UNN9yg5uZmeb3eWI6fkPrzb2zGjBnat29fNBwlae/evcrKyiJEBll/jtexY8fOCI7TIWkN9V9DZ/oO2sG0ceNGy+l0Wi+99JK1c+dO65577rFGjx5t+f1+y7Isa968edbixYuj6z/66CNrxIgR1hNPPGHt2rXLqqio4KO9MWT3eK1Zs8ZKSUmx3nzzTevQoUPRR3t7u6mXkFDsHq/v4tM0sWf3mB08eNBKTU217r//fmvPnj3We++9Z2VkZFiPPPKIqZeQUOwer4qKCis1NdX64x//aO3fv9/64IMPrAkTJli33XabqZfQZ8M6RizLsp5++mnrBz/4gZWSkmLl5eVZ//jHP6J/d91111nz58/vtv7111+3Jk6caKWkpFg//vGPrc2bN8d44sRm53j98Ic/tCSd8aioqIj94AnK7r+vbyNGzLB7zD7++GMrPz/fcjqd1sUXX2w9+uij1smTJ2M8deKyc7xOnDhhrVixwpowYYLlcrksr9dr3XfffdZ//vMfA5Pbk2RZQ/3cDQAAGM6G7T0jAAAgPhAjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACj/j9wbzTMl1qC2gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(emb[:, 0], emb[:, 1], 'o', linewidth=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By setting a threshold value of 0.01, we determine which nodes belong to the given community."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "communities = [set(np.where(emb[:,ith]>0.01)[0]) for ith in range(2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "\n",
+    "for ith, community in enumerate(communities):\n",
+    "    cols = [\"red\" if node in community else \"blue\" for node in G.nodes]\n",
+    "    plt.subplot(1,3,ith+1)\n",
+    "    plt.title(f\"Community {ith}\")\n",
+    "    nx.draw_spring(G, node_color=cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although the example above does not show this, in general also this clustering method may be non-mutually exclusive, and nodes may belong to more than one community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Louvain and Modularity Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we use the Louvain method, which is one of the most popular methods for performing community detection, even on fairly large graphs. As described in the chapter, the Louvain method basically optimize the partitioning (it is a mutually exclusing community detection algorithm), identifying the one that maximize the modularity score, meaning that nodes belonging to the same community are very well connected among themself, and weakly connected to the other communities. \n",
+    "\n",
+    "**Louvain, unlike other community detection algorithms, does not require to specity the number of communities in advance and find the best, optimal number of communities.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from communities.algorithms import louvain_method\n",
+    "communities = louvain_method(adj)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values\n",
+    "nx.draw_spring(G, node_color=c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},\n",
+       " {10, 11, 12, 13},\n",
+       " {14, 15, 16, 17, 18, 19, 20, 21, 22, 23}]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Girvan Newman"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Girvan–Newman algorithm detects communities by progressively removing edges from the original graph. The algorithm removes the “most valuable” edge, traditionally the edge with the highest betweenness centrality, at each step. As the graph breaks down into pieces, the tightly knit community structure is exposed and the result can be depicted as a dendrogram.\n",
+    "\n",
+    "**BE AWARE that because of the betweeness centrality computation, this method may not scale well on large graphs**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from communities.algorithms import girvan_newman\n",
+    "communities = girvan_newman(adj, n=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = pd.Series({node: colors[ith] for ith, nodes in enumerate(communities) for node in nodes}).values\n",
+    "nx.draw_spring(G, node_color=c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n",
+       " {12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap6",
+   "language": "python",
+   "name": "chap6"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter06/03_graph_similarity.ipynb b/Chapter06/03_graph_similarity.ipynb
new file mode 100644
index 0000000..258c74a
--- /dev/null
+++ b/Chapter06/03_graph_similarity.ipynb
@@ -0,0 +1,691 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "9LbvNo43_S8s"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# install PyTorch Geometric if running on Google Colab\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "  !pip install node2vec\n",
+    "  import torch\n",
+    "\n",
+    "  def format_pytorch_version(version):\n",
+    "    return version.split('+')[0]\n",
+    "\n",
+    "  TORCH_version = torch.__version__\n",
+    "  TORCH = format_pytorch_version(TORCH_version)\n",
+    "\n",
+    "  def format_cuda_version(version):\n",
+    "    return 'cu' + version.replace('.', '')\n",
+    "\n",
+    "  CUDA_version = torch.version.cuda\n",
+    "  CUDA = format_cuda_version(CUDA_version)\n",
+    "\n",
+    "  !pip install torch-scatter     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
+    "  !pip install torch-sparse      -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
+    "  !pip install torch-cluster     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
+    "  !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
+    "  !pip install torch-geometric"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BIK1pgU4_lWd"
+   },
+   "source": [
+    "## Graph embedding-based methods\n",
+    "\n",
+    "Such techniques seek to apply graph embedding techniques to obtain node-level or graph-level representations and further use the representations for similarity learning. For example, DeepWalk and Node2Vec can be used to extract meaningful embedding that can then be used to define a similarity function or to predict similarity scores. For example, in Tixier et al. (2015), node2vec was used for encoding node embeddings for representing a graph as an image. Specifically, two-dimensional (2D) histograms obtained from those node embeddings were passed to a classical 2D convolutional neural network (CNN) architecture designed for images. Such a simple yet powerful approach enabled good results to be obtained for many benchmark datasets."
+   ]
+  },
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+   "cell_type": "code",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20, Loss: 0.6815312504768372\n",
+      "Epoch 2/20, Loss: 0.5630680322647095\n",
+      "Epoch 3/20, Loss: 0.41003280878067017\n",
+      "Epoch 4/20, Loss: 0.32633399963378906\n",
+      "Epoch 5/20, Loss: 0.22436682879924774\n",
+      "Epoch 6/20, Loss: 0.09085053205490112\n",
+      "Epoch 7/20, Loss: 0.04055032134056091\n",
+      "Epoch 8/20, Loss: 0.013849971815943718\n",
+      "Epoch 9/20, Loss: 0.008305830880999565\n",
+      "Epoch 10/20, Loss: 0.003659198060631752\n",
+      "Epoch 11/20, Loss: 0.0010380035964772105\n",
+      "Epoch 12/20, Loss: 0.00029954835190437734\n",
+      "Epoch 13/20, Loss: 0.00010289005876984447\n",
+      "Epoch 14/20, Loss: 8.603346213931218e-05\n",
+      "Epoch 15/20, Loss: 1.873927794804331e-05\n",
+      "Epoch 16/20, Loss: 3.4570541629364016e-06\n",
+      "Epoch 17/20, Loss: 1.0967236221404164e-06\n",
+      "Epoch 18/20, Loss: 1.7404512391294702e-06\n",
+      "Epoch 19/20, Loss: 5.412063728726935e-06\n",
+      "Epoch 20/20, Loss: 7.152556946721234e-08\n",
+      "Test Accuracy: 0.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Method 1: Graph Embedding-based (Node2Vec)\n",
+    "import networkx as nx\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "#from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# Load necessary libraries for each method\n",
+    "from node2vec import Node2Vec\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "# Create toy dataset with simple graphs\n",
+    "num_graphs = 10\n",
+    "graphs = [nx.erdos_renyi_graph(10, np.random.rand()) for _ in range(num_graphs)]\n",
+    "\n",
+    "# Labels for graph similarity task (could be used for graph classification or regression)\n",
+    "labels = [np.random.choice([0,1]) for _ in range(num_graphs)]\n",
+    "\n",
+    "# Function to generate 2D histogram from node embeddings\n",
+    "def generate_2d_histogram(node_embeddings, bins=16):\n",
+    "    # Flatten embeddings to create histograms\n",
+    "    embeddings = np.vstack(node_embeddings)\n",
+    "    histogram, xedges, yedges = np.histogram2d(embeddings[:, 0], embeddings[:, 1], bins=bins)\n",
+    "    return histogram\n",
+    "\n",
+    "# Prepare graph-level 2D histograms from node embeddings\n",
+    "graph_histograms = []\n",
+    "for i, graph in enumerate(graphs):\n",
+    "    node2vec = Node2Vec(graph, dimensions=64, walk_length=10, num_walks=80, workers=4)\n",
+    "    model = node2vec.fit()\n",
+    "    node_embeddings = [model.wv.get_vector(str(node)) for node in graph.nodes()]\n",
+    "    histogram = generate_2d_histogram(node_embeddings)\n",
+    "    graph_histograms.append(histogram)\n",
+    "\n",
+    "# Convert histograms into tensors for CNN\n",
+    "graph_histograms = np.array(graph_histograms)\n",
+    "graph_histograms = torch.tensor(graph_histograms, dtype=torch.float32)\n",
+    "\n",
+    "# Split histograms into training and testing sets\n",
+    "train_histograms, test_histograms, train_labels, test_labels = train_test_split(graph_histograms, labels, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Define a simple 2D CNN model for graph classification\n",
+    "class GraphCNN(nn.Module):\n",
+    "    def __init__(self, input_channels, num_classes):\n",
+    "        super(GraphCNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, stride=1, padding=1)\n",
+    "        self.pool = nn.MaxPool2d(2, 2)\n",
+    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n",
+    "        self.fc1 = nn.Linear(1024, 128)\n",
+    "        self.fc2 = nn.Linear(128, num_classes)\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.dropout = nn.Dropout(0.5)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.relu(self.conv1(x))\n",
+    "        x = self.pool(x)\n",
+    "        x = self.relu(self.conv2(x))\n",
+    "        x = self.pool(x)\n",
+    "        x = x.view(5, -1)\n",
+    "        x = self.relu(self.fc1(x))\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "\n",
+    "# Initialize and train the CNN model\n",
+    "num_classes = 2  # Binary classification\n",
+    "input_channels = 1  # Single channel for histogram\n",
+    "bins = 16  # Same as used in generate_2d_histogram function\n",
+    "\n",
+    "train_histograms = train_histograms.unsqueeze(1)  # Add channel dimension\n",
+    "test_histograms = test_histograms.unsqueeze(1)\n",
+    "\n",
+    "cnn_model = GraphCNN(input_channels, num_classes)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(cnn_model.parameters(), lr=0.001)\n",
+    "\n",
+    "# Training loop\n",
+    "cnn_model.train()\n",
+    "epochs = 20\n",
+    "for epoch in range(epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    outputs = cnn_model(train_histograms)\n",
+    "    loss = criterion(outputs, torch.tensor(train_labels, dtype=torch.long))\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    print(f\"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}\")\n",
+    "\n",
+    "# Evaluate on test set\n",
+    "cnn_model.eval()\n",
+    "with torch.no_grad():\n",
+    "    test_outputs = cnn_model(test_histograms)\n",
+    "    predictions = torch.argmax(test_outputs, axis=1)\n",
+    "    accuracy = accuracy_score(test_labels, predictions.numpy())\n",
+    "    print(f\"Test Accuracy: {accuracy}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PpI9k7-N_url"
+   },
+   "source": [
+    "## Graph kernel-based methods\n",
+    "\n",
+    "Graph kernel-based methods have generated a lot of interest in terms of capturing the similarity between graphs. These approaches compute the similarity between two graphs as a function of the similarities between some of their substructures. Different graph kernels exist based on the substructures they use, which include random walks, shortest paths, and subgraphs. As an example, a method called Deep Graph Kernels (DGK) (Yanardag et al., 2015) decomposes graphs into substructures that are viewed as \"words\". Then, natural language processing (NLP) approaches such as continuous bag of words (CBOW) and skip-gram are used to learn latent representations of the substructures. This way, the kernel between two graphs is defined based on the similarity of the substructure space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a4beFzLP_c4c",
+    "outputId": "89cfb937-f80d-4168-9ab2-06ebb4ebbcd4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy using Deep Graph Kernels (DGK): 0.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "import networkx as nx\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "\n",
+    "# Create toy dataset with simple graphs\n",
+    "num_graphs = 10\n",
+    "graphs = [nx.erdos_renyi_graph(50, np.random.rand()) for _ in range(num_graphs)]\n",
+    "\n",
+    "# Labels for graph similarity task (could be used for graph classification or regression)\n",
+    "labels = [np.random.choice([0,1]) for _ in range(num_graphs)]\n",
+    "\n",
+    "# Decompose graphs into substructures using random walks\n",
+    "def random_walks_as_words(graph, walk_length=6, num_walks=10):\n",
+    "    \"\"\"Generates 'words' from random walks on the graph.\"\"\"\n",
+    "    walks = []\n",
+    "    for _ in range(num_walks):\n",
+    "        for node in graph.nodes():\n",
+    "            walk = [str(node)]\n",
+    "            for _ in range(walk_length - 1):\n",
+    "                neighbors = list(graph.neighbors(int(walk[-1])))\n",
+    "                if neighbors:\n",
+    "                    walk.append(str(np.random.choice(neighbors)))\n",
+    "                else:\n",
+    "                    break\n",
+    "            walks.append(\" \".join(walk))\n",
+    "    return walks\n",
+    "\n",
+    "# Create a \"document\" for each graph using its random walks\n",
+    "graph_documents = []\n",
+    "for graph in graphs:\n",
+    "    walks = random_walks_as_words(graph)\n",
+    "    graph_documents.append(\" \".join(walks))  # Combine all walks into a single document\n",
+    "\n",
+    "# Use CountVectorizer to generate a \"bag of words\" representation for each graph\n",
+    "vectorizer = CountVectorizer()\n",
+    "graph_bow = vectorizer.fit_transform(graph_documents)  # Sparse matrix of shape (num_graphs, num_features)\n",
+    "\n",
+    "# Compute pairwise similarities between graphs using cosine similarity\n",
+    "graph_similarity_matrix = cosine_similarity(graph_bow)\n",
+    "\n",
+    "# Use the similarity matrix as input features for a classification task\n",
+    "# Split dataset into training and testing sets\n",
+    "train_indices, test_indices = train_test_split(range(len(graphs)), test_size=0.5, random_state=42)\n",
+    "\n",
+    "train_similarity = graph_similarity_matrix[np.ix_(train_indices, train_indices)]\n",
+    "test_similarity = graph_similarity_matrix[np.ix_(test_indices, train_indices)]  # Test against training similarities\n",
+    "\n",
+    "train_labels = [labels[i] for i in train_indices]\n",
+    "test_labels = [labels[i] for i in test_indices]\n",
+    "\n",
+    "# Train a classifier using the training similarity matrix\n",
+    "svm = SVC(kernel=\"precomputed\")  # Precomputed kernel\n",
+    "svm.fit(train_similarity, train_labels)\n",
+    "\n",
+    "# Predict on the test set\n",
+    "test_predictions = svm.predict(test_similarity)\n",
+    "\n",
+    "# Evaluate the classification accuracy\n",
+    "accuracy = accuracy_score(test_labels, test_predictions)\n",
+    "print(f\"Test Accuracy using Deep Graph Kernels (DGK): {accuracy}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uY5Ysjt-_6eu"
+   },
+   "source": [
+    "## GNN-based methods\n",
+    "\n",
+    "With the emergence of deep learning (DL) techniques, GNNs have become a powerful new tool for learning representations on graphs. Such powerful models can be easily adapted to various tasks, including graph similarity learning. Furthermore, they present a key advantage with respect to other traditional graph embedding approaches. Indeed, while the latter generally learn the representation in an isolated stage, in this kind of approach, the representation learning and the target learning task are conducted jointly. Therefore, the GNN deep models can better leverage the graph features for the specific learning task. We have already seen an example of similarity learning using GNNs in Chapter 4, Unsupervised Graph Learning, where a two-branch network was trained to estimate the proximity distance between two graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7PBnQ_Du_gMu"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch_geometric.nn import GCNConv, global_mean_pool\n",
+    "from torch_geometric.data import Data, DataLoader\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import networkx as nx\n",
+    "import numpy as np\n",
+    "\n",
+    "# Create toy dataset with simple graphs\n",
+    "num_graphs = 10\n",
+    "graphs = [nx.erdos_renyi_graph(10, np.random.rand()) for _ in range(num_graphs)]\n",
+    "\n",
+    "# Generate similarity labels (e.g., based on structural similarity)\n",
+    "labels = [np.random.rand() for _ in range(num_graphs)]  # Similarity score between 0 and 1\n",
+    "\n",
+    "# Convert graphs to PyTorch Geometric Data objects\n",
+    "def nx_to_data(graph, label):\n",
+    "    edge_index = torch.tensor(list(graph.edges), dtype=torch.long).t().contiguous()\n",
+    "    x = torch.eye(graph.number_of_nodes(), dtype=torch.float)  # Node features as identity matrix\n",
+    "    return Data(x=x, edge_index=edge_index, y=torch.tensor([label], dtype=torch.float))\n",
+    "\n",
+    "data_list = [nx_to_data(graph, labels[i]) for i, graph in enumerate(graphs)]\n",
+    "\n",
+    "# Split dataset into training and testing sets\n",
+    "train_data, test_data = train_test_split(data_list, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Create DataLoaders for batching\n",
+    "train_loader = DataLoader(train_data, batch_size=2, shuffle=True)\n",
+    "test_loader = DataLoader(test_data, batch_size=2, shuffle=False)\n",
+    "\n",
+    "# Define a GNN model for graph-level representation\n",
+    "class GNN(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
+    "        super(GNN, self).__init__()\n",
+    "        self.conv1 = GCNConv(input_dim, hidden_dim)\n",
+    "        self.conv2 = GCNConv(hidden_dim, hidden_dim)\n",
+    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
+    "        self.relu = nn.ReLU()\n",
+    "\n",
+    "    def forward(self, x, edge_index, batch):\n",
+    "        x = self.relu(self.conv1(x, edge_index))\n",
+    "        x = self.relu(self.conv2(x, edge_index))\n",
+    "        x = global_mean_pool(x, batch)  # Graph-level pooling\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "\n",
+    "# Define a two-branch similarity learning network\n",
+    "class SimilarityNet(nn.Module):\n",
+    "    def __init__(self, gnn, embedding_dim):\n",
+    "        super(SimilarityNet, self).__init__()\n",
+    "        self.gnn = gnn\n",
+    "        self.fc = nn.Linear(2 * embedding_dim, 1)  # Combine embeddings from two branches\n",
+    "\n",
+    "    def forward(self, data1, data2):\n",
+    "        # Extract graph-level embeddings for both graphs\n",
+    "        x1 = self.gnn(data1.x, data1.edge_index, data1.batch)\n",
+    "        x2 = self.gnn(data2.x, data2.edge_index, data2.batch)\n",
+    "        # Concatenate embeddings and pass through a fully connected layer\n",
+    "        x = torch.cat([x1, x2], dim=1)\n",
+    "        similarity_score = self.fc(x)\n",
+    "        return similarity_score\n",
+    "\n",
+    "# Initialize models and hyperparameters\n",
+    "input_dim = 10  # Number of node features\n",
+    "hidden_dim = 32\n",
+    "embedding_dim = 16\n",
+    "gnn = GNN(input_dim, hidden_dim, embedding_dim)\n",
+    "model = SimilarityNet(gnn, embedding_dim)\n",
+    "criterion = nn.MSELoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "\n",
+    "# Training loop\n",
+    "model.train()\n",
+    "epochs = 20\n",
+    "for epoch in range(epochs):\n",
+    "    epoch_loss = 0\n",
+    "    for data1, data2 in zip(train_loader, train_loader):  # Pair graphs for similarity learning\n",
+    "        optimizer.zero_grad()\n",
+    "        pred = model(data1, data2)\n",
+    "        target = (data1.y + data2.y) / 2  # Example: Average similarity score\n",
+    "        loss = criterion(pred.view(-1), target.view(-1))\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        epoch_loss += loss.item()\n",
+    "    print(f\"Epoch {epoch+1}/{epochs}, Loss: {epoch_loss / len(train_loader)}\")\n",
+    "\n",
+    "# Evaluate the model\n",
+    "model.eval()\n",
+    "predictions, targets = [], []\n",
+    "with torch.no_grad():\n",
+    "    for data1, data2 in zip(test_loader, test_loader):\n",
+    "        pred = model(data1, data2)\n",
+    "        target = (data1.y + data2.y) / 2\n",
+    "        predictions.extend(pred.view(-1).tolist())\n",
+    "        targets.extend(target.view(-1).tolist())\n",
+    "\n",
+    "mse = mean_squared_error(targets, predictions)\n",
+    "print(f\"Test Mean Squared Error: {mse}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tCfFB-iv4Ihd"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "chap6",
+   "language": "python",
+   "name": "chap6"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
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+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = "~3.8"
+content-hash = "1a65cb0ef1b94d242aca58208644913179644f66299c8c22fd00cd5f8b1ecbf3"
diff --git a/Chapter06/pyproject.toml b/Chapter06/pyproject.toml
new file mode 100644
index 0000000..b134975
--- /dev/null
+++ b/Chapter06/pyproject.toml
@@ -0,0 +1,37 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 6"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+networkx = "==2.5" 
+matplotlib = "==3.2.2"
+node2vec = "==0.3.3"
+karateclub = "==1.0.19"
+gensim = "==3.8.3"
+communities = "==2.2.0"
+scikit-learn = "==0.24.0" 
+chardet = "==5.2.0"
+# Tensorflow
+tensorflow = "^2.6.0" 
+tensorflow-io-gcs-filesystem = "==0.21.0" # This needs pinning - see https://stackoverflow.com/a/76477590 
+protobuf= "^3.20"
+# Torch
+torch = "^2.1.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+torch-scatter = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+# Graph ML Libraries
+nxt-gem = { git="https://github.com/palash1992/GEM.git", branch="master" }
+stellargraph = { git="https://github.com/stellargraph/stellargraph.git", branch="develop" }
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/Chapter06/requirements.txt b/Chapter06/requirements.txt
new file mode 100644
index 0000000..3e1f9f7
--- /dev/null
+++ b/Chapter06/requirements.txt
@@ -0,0 +1,136 @@
+absl-py==2.2.2 ; python_version == "3.8"
+aiohappyeyeballs==2.4.4 ; python_version == "3.8"
+aiohttp==3.10.11 ; python_version == "3.8"
+aiosignal==1.3.1 ; python_version == "3.8"
+appnope==0.1.4 ; (platform_system == "Darwin" or sys_platform == "darwin") and python_version == "3.8"
+asttokens==3.0.0 ; python_version == "3.8"
+astunparse==1.6.3 ; python_version == "3.8"
+async-timeout==5.0.1 ; python_version == "3.8"
+attrs==25.3.0 ; python_version == "3.8"
+backcall==0.2.0 ; python_version == "3.8"
+cachetools==5.5.2 ; python_version == "3.8"
+certifi==2025.1.31 ; python_version == "3.8"
+cffi==1.17.1 ; implementation_name == "pypy" and python_version == "3.8"
+chardet==5.2.0 ; python_version == "3.8"
+charset-normalizer==3.4.1 ; python_version == "3.8"
+colorama==0.4.6 ; python_version == "3.8" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version == "3.8"
+communities==2.2.0 ; python_version == "3.8"
+cycler==0.12.1 ; python_version == "3.8"
+cython==0.29.14 ; python_version == "3.8"
+debugpy==1.8.13 ; python_version == "3.8"
+decorator==5.2.1 ; python_version == "3.8"
+executing==2.2.0 ; python_version == "3.8"
+filelock==3.16.1 ; python_version == "3.8"
+flatbuffers==2.0.7 ; python_version == "3.8"
+frozenlist==1.5.0 ; python_version == "3.8"
+fsspec==2025.3.0 ; python_version == "3.8"
+gast==0.4.0 ; python_version == "3.8"
+gensim==3.8.3 ; python_version == "3.8"
+google-auth-oauthlib==1.0.0 ; python_version == "3.8"
+google-auth==2.38.0 ; python_version == "3.8"
+google-pasta==0.2.0 ; python_version == "3.8"
+grpcio==1.70.0 ; python_version == "3.8"
+h5py==3.11.0 ; python_version == "3.8"
+idna==3.10 ; python_version == "3.8"
+importlib-metadata==8.5.0 ; python_version == "3.8"
+ipykernel==6.29.5 ; python_version == "3.8"
+ipython==8.12.3 ; python_version == "3.8"
+jedi==0.19.2 ; python_version == "3.8"
+jinja2==3.1.6 ; python_version == "3.8"
+joblib==1.4.2 ; python_version == "3.8"
+jupyter-client==8.6.3 ; python_version == "3.8"
+jupyter-core==5.7.2 ; python_version == "3.8"
+karateclub==1.0.19 ; python_version == "3.8"
+keras-preprocessing==1.1.2 ; python_version == "3.8"
+keras==2.7.0 ; python_version == "3.8"
+kiwisolver==1.4.7 ; python_version == "3.8"
+libclang==18.1.1 ; python_version == "3.8"
+lightning-utilities==0.11.9 ; python_version == "3.8"
+markdown==3.7 ; python_version == "3.8"
+markupsafe==2.1.5 ; python_version == "3.8"
+matplotlib-inline==0.1.7 ; python_version == "3.8"
+matplotlib==3.2.2 ; python_version == "3.8"
+mpmath==1.3.0 ; python_version == "3.8"
+multidict==6.1.0 ; python_version == "3.8"
+nest-asyncio==1.6.0 ; python_version == "3.8"
+networkx==2.5 ; python_version == "3.8"
+node2vec==0.3.3 ; python_version == "3.8"
+numpy==1.24.4 ; python_version == "3.8"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-nvjitlink-cu12==12.8.93 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+nxt-gem @ git+https://github.com/palash1992/GEM.git@ae8e92d34213f5785757b4a0943bd7d8d337adb3 ; python_version == "3.8"
+oauthlib==3.2.2 ; python_version == "3.8"
+opt-einsum==3.4.0 ; python_version == "3.8"
+packaging==24.2 ; python_version == "3.8"
+pandas==2.0.3 ; python_version == "3.8"
+parso==0.8.4 ; python_version == "3.8"
+pexpect==4.9.0 ; sys_platform != "win32" and python_version == "3.8"
+pickleshare==0.7.5 ; python_version == "3.8"
+pillow==10.4.0 ; python_version == "3.8"
+platformdirs==4.3.6 ; python_version == "3.8"
+prompt-toolkit==3.0.50 ; python_version == "3.8"
+propcache==0.2.0 ; python_version == "3.8"
+protobuf==3.20.3 ; python_version == "3.8"
+psutil==7.0.0 ; python_version == "3.8"
+ptyprocess==0.7.0 ; sys_platform != "win32" and python_version == "3.8"
+pure-eval==0.2.3 ; python_version == "3.8"
+pyasn1-modules==0.4.2 ; python_version == "3.8"
+pyasn1==0.6.1 ; python_version == "3.8"
+pycparser==2.22 ; implementation_name == "pypy" and python_version == "3.8"
+pygments==2.19.1 ; python_version == "3.8"
+pygsp==0.5.1 ; python_version == "3.8"
+pyparsing==3.1.4 ; python_version == "3.8"
+python-dateutil==2.9.0.post0 ; python_version == "3.8"
+python-louvain==0.16 ; python_version == "3.8"
+pytz==2025.2 ; python_version == "3.8"
+pywin32==310 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version == "3.8"
+pyzmq==26.4.0 ; python_version == "3.8"
+requests-oauthlib==2.0.0 ; python_version == "3.8"
+requests==2.32.3 ; python_version == "3.8"
+rsa==4.9 ; python_version == "3.8"
+scikit-learn==0.24.0 ; python_version == "3.8"
+scipy==1.10.1 ; python_version == "3.8"
+setuptools==75.3.2 ; python_version == "3.8"
+six==1.17.0 ; python_version == "3.8"
+smart-open==7.1.0 ; python_version == "3.8"
+stack-data==0.6.3 ; python_version == "3.8"
+stellargraph @ git+https://github.com/stellargraph/stellargraph.git@3c2c8c18ab4c5c16660f350d8e23d7dc39e738de ; python_version == "3.8"
+sympy==1.13.3 ; python_version == "3.8"
+tensorboard-data-server==0.7.2 ; python_version == "3.8"
+tensorboard==2.14.0 ; python_version == "3.8"
+tensorflow-estimator==2.7.0 ; python_version == "3.8"
+tensorflow-io-gcs-filesystem==0.21.0 ; python_version == "3.8"
+tensorflow==2.7.2 ; python_version == "3.8"
+termcolor==2.4.0 ; python_version == "3.8"
+theano==1.0.5 ; python_version == "3.8"
+threadpoolctl==3.5.0 ; python_version == "3.8"
+torch-geometric==2.6.1 ; python_version == "3.8"
+torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version == "3.8"
+torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version == "3.8"
+torch==2.1.2 ; python_version == "3.8"
+torchmetrics==1.5.2 ; python_version == "3.8"
+torchvision==0.16.2 ; python_version == "3.8"
+tornado==6.4.2 ; python_version == "3.8"
+tqdm==4.67.1 ; python_version == "3.8"
+traitlets==5.14.3 ; python_version == "3.8"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
+typing-extensions==4.13.1 ; python_version == "3.8"
+tzdata==2025.2 ; python_version == "3.8"
+urllib3==2.2.3 ; python_version == "3.8"
+wcwidth==0.2.13 ; python_version == "3.8"
+werkzeug==3.0.6 ; python_version == "3.8"
+wheel==0.45.1 ; python_version == "3.8"
+wrapt==1.17.2 ; python_version == "3.8"
+yarl==1.15.2 ; python_version == "3.8"
+zipp==3.20.2 ; python_version == "3.8"
diff --git a/Chapter06/01_Social_network_analysis.ipynb b/Chapter07/01_Social_network_analysis.ipynb
similarity index 97%
rename from Chapter06/01_Social_network_analysis.ipynb
rename to Chapter07/01_Social_network_analysis.ipynb
index 535a774..33b41b6 100644
--- a/Chapter06/01_Social_network_analysis.ipynb
+++ b/Chapter07/01_Social_network_analysis.ipynb
@@ -296,7 +296,10 @@
    "outputs": [],
    "source": [
     "plt.axis(\"off\")\n",
-    "nx.draw_networkx(G, pos=spring_pos, node_color=default_node_color, edge_color=default_edge_color, with_labels=False, node_size=35)"
+    "plt.figure(1,figsize=(12,12)) \n",
+    "nx.draw_networkx(G, pos=spring_pos, node_color=default_node_color, edge_color=default_edge_color, with_labels=False, node_size=35)\n",
+    "ax = plt.gca() # to get the current axis\n",
+    "ax.collections[0].set_edgecolor(\"#fff\")"
    ]
   },
   {
@@ -341,7 +344,10 @@
     "                         pos=spring_pos, \n",
     "                         nodelist=max_keys, \n",
     "                         node_color=enhanced_edge_color,\n",
-    "                         node_size=max_vals)"
+    "                         node_size=max_vals)\n",
+    "  \n",
+    "  ax = plt.gca() # to get the current axis\n",
+    "  ax.collections[0].set_edgecolor(\"#fff\")"
    ]
   },
   {
@@ -675,14 +681,25 @@
     "  print(node, \"is in community number\", parts.get(node))\n",
     "  \n",
     "n_sizes = [5]*len(G.nodes())\n",
+    "\n",
+    "plt.axis(\"off\")\n",
+    "nx.draw_networkx(G, pos=spring_pos, cmap=plt.get_cmap(\"Blues\"), edge_color=default_edge_color, node_color=values, node_size=n_sizes, with_labels=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# enhance color and size of the ego-nodes\n",
     "for node in ego_nodes:\n",
     "  n_sizes[node] = 250\n",
     "\n",
     "plt.axis(\"off\")\n",
     "nx.draw_networkx(G, pos=spring_pos, cmap=plt.get_cmap(\"Blues\"), edge_color=default_edge_color, node_color=values, node_size=n_sizes, with_labels=False)\n",
     "\n",
-    "# enhance color and size of the ego-nodes\n",
-    "nodes = nx.draw_networkx_nodes(G,spring_pos,ego_nodes,node_color=[parts.get(node) for node in ego_nodes])\n",
+    "nodes = nx.draw_networkx_nodes(G, spring_pos, ego_nodes,node_color=[parts.get(node) for node in ego_nodes])\n",
     "nodes.set_edgecolor(enhanced_node_color)"
    ]
   },
@@ -875,7 +892,7 @@
     "        for n2 in circles[j]:\n",
     "          if n1 == n2:\n",
     "            print(n1, 'present in ',i,'found in', j)\n",
-    "            assert(False)"
+    "            # assert(False)"
    ]
   },
   {
@@ -1084,6 +1101,19 @@
     "G.nodes[0]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dump the graph on file\n",
+    "import pickle\n",
+    "\n",
+    "with open('test.gpickle', 'wb') as f:\n",
+    "    pickle.dump(G, f, pickle.HIGHEST_PROTOCOL)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1096,35 +1126,6 @@
     "More in detail, TODO"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "executionInfo": {
-     "elapsed": 14891,
-     "status": "ok",
-     "timestamp": 1616871410804,
-     "user": {
-      "displayName": "Aldo Marzullo",
-      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjBD_mZewcZ8LCqkD20Nku4DR5OCGFqYkxawoUjgg=s64",
-      "userId": "17245895923239449231"
-     },
-     "user_tz": -60
-    },
-    "id": "Li-IypwmIW9V",
-    "outputId": "580084bf-ff08-406d-b335-7f791b93e9fe"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install stellargraph\n",
-    "!pip install node2vec==0.3.3\n",
-    "!pip install git+https://github.com/palash1992/GEM.git"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1710,9 +1711,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "id": "O1YkgCx5rh25"
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -1726,9 +1725,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "chap7",
    "language": "python",
-   "name": "python3"
+   "name": "chap7"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1740,9 +1739,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.8.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/Chapter07/01_nlp_graph_creation.ipynb b/Chapter07/01_nlp_graph_creation.ipynb
deleted file mode 100644
index 6801cba..0000000
--- a/Chapter07/01_nlp_graph_creation.ipynb
+++ /dev/null
@@ -1,4672 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Text Analytics and Natural Language Processing using Graphs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following we will focus on analyzing textual documents and leverage on graph analysis in order to identify insight and extract relevant information. \n",
-    "\n",
-    "In particular in the following we will show you how to:\n",
-    "\n",
-    "* Extract structured information from text by using NLP techniques and models\n",
-    "* Build different type of graphs starting from the information extracted in the previous point\n",
-    "* Analyze the graph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "import pandas as pd\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nltk.corpus import reuters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = pd.DataFrame([\n",
-    "    {\"id\": _id, \"clean_text\": reuters.raw(_id).replace(\"\\n\", \"\"), \"label\": reuters.categories(_id)}\n",
-    "    for _id in reuters.fileids()\n",
-    "]).set_index(\"id\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SUBROTO SAYS INDONESIA SUPPORTS TIN PACT EXTENSION  Mines and Energy Minister Subroto  confirmed Indonesian support for an extension of the sixth  International Tin Agreement (ITA), but said a new pact was not  necessary.      Asked by Reuters to clarify his statement on Monday in  which he said the pact should be allowed to lapse, Subroto said  Indonesia was ready to back extension of the ITA.      \"We can support extension of the sixth agreement,\" he said.  \"But a seventh accord we believe to be unnecessary.\"      The sixth ITA will expire at the end of June unless a  two-thirds majority of members vote for an extension.  '"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.iloc[10][\"clean_text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "90"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import Counter\n",
-    "len(Counter([label for document_labels in corpus[\"label\"] for label in document_labels]).most_common())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label  \n",
-       "id                                                          \n",
-       "test/14826                                         [trade]  \n",
-       "test/14828                                         [grain]  \n",
-       "test/14829                                [crude, nat-gas]  \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]  \n",
-       "test/14833                             [palm-oil, veg-oil]  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Language Detection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import langdetect"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def getLanguage(text: str):\n",
-    "    try:\n",
-    "        return langdetect.detect(text)\n",
-    "    except: \n",
-    "        return np.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[\"language\"] = corpus[\"clean_text\"].apply(getLanguage)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "en    9899\n",
-       "sv     432\n",
-       "de     371\n",
-       "sw      29\n",
-       "so      23\n",
-       "pt       7\n",
-       "nl       7\n",
-       "vi       6\n",
-       "et       5\n",
-       "ca       2\n",
-       "Name: language, dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus[\"language\"].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "      <th>language</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label language  \n",
-       "id                                                                   \n",
-       "test/14826                                         [trade]       en  \n",
-       "test/14828                                         [grain]       en  \n",
-       "test/14829                                [crude, nat-gas]       en  \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en  \n",
-       "test/14833                             [palm-oil, veg-oil]       en  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using fasttext"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
-      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100  916k  100  916k    0     0   547k      0  0:00:01  0:00:01 --:--:--  548k\n"
-     ]
-    }
-   ],
-   "source": [
-    "!curl -w GET https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz > lid.176.ftz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import fasttext\n",
-    "\n",
-    "m = fasttext.load_model(\"lid.176.ftz\")\n",
-    "def getLanguage(text: str):\n",
-    "    return m.predict(text)[0][0].replace(\"__label__\", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[\"language\"] = corpus[\"clean_text\"].apply(getLanguage)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "en    10278\n",
-       "de       90\n",
-       "ja       73\n",
-       "it       67\n",
-       "sv       52\n",
-       "zh       48\n",
-       "es       31\n",
-       "fr       27\n",
-       "eu       20\n",
-       "eo       12\n",
-       "Name: language, dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus[\"language\"].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'USDA - U.S. 1986/87 ENDING CORN STOCKS  5,240 MLN BU, WHEAT 1,848 MLN, SOYBEANS 610 MLN  USDA - U.S. 1986/87 ENDING CORN STOCKS  5,240 MLN BU, WHEAT 1,848 MLN, SOYBEANS 610 MLN  '"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus[corpus[\"language\"]==\"ja\"].iloc[5][\"clean_text\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NLP Enrichment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import spacy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to download the model from the Spacy library, please issue the following command in a shell \n",
-    "\n",
-    "\n",
-    "<pre><code>\n",
-    "python -m spacy download en_core_web_md\n",
-    "</code></pre>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nlp = spacy.load('en_core_web_md')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[\"parsed\"] = corpus[\"clean_text\"].apply(nlp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Thailand's trade deficit widened to 4.5  billion baht in the first quarter of 1987 from 2.1 billion a  year ago, the Business Economics Department said.      It said Janunary/March imports rose to 65.1 billion baht  from 58.7 billion. Thailand's improved business climate this  year resulted in a 27 pct increase in imports of raw materials  and semi-finished products.      The country's oil import bill, however, fell 23 pct in the  first quarter due to lower oil prices.      The department said first quarter exports expanded to 60.6  billion baht from 56.6 billion.      Export growth was smaller than expected due to lower  earnings from many key commodities including rice whose  earnings declined 18 pct, maize 66 pct, sugar 45 pct, tin 26  pct and canned pineapples seven pct.      Products registering high export growth were jewellery up  64 pct, clothing 57 pct and rubber 35 pct.  \""
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.loc[\"test/14832\"][\"clean_text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from spacy import displacy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">THAI TRADE DEFICIT WIDENS IN \n",
-       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    FIRST QUARTER\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
-       "</mark>\n",
-       "  \n",
-       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    Thailand\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
-       "</mark>\n",
-       "'s trade deficit widened to \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    4.5  billion baht\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " in \n",
-       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    the first quarter of 1987\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
-       "</mark>\n",
-       " from \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    2.1 billion\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " a  year ago, \n",
-       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    the Business Economics Department\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
-       "</mark>\n",
-       " said.      It said \n",
-       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    Janunary\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
-       "</mark>\n",
-       "/March imports rose to \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    65.1 billion baht\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       "  from \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    58.7 billion\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ". \n",
-       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    Thailand\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
-       "</mark>\n",
-       "'s improved business climate \n",
-       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    this  year\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
-       "</mark>\n",
-       " resulted in a \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    27 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " increase in imports of raw materials  and semi-finished products.      The country's oil import bill, however, fell \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    23 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " in \n",
-       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    the  first quarter\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
-       "</mark>\n",
-       " due to lower oil prices.      The department said \n",
-       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    first quarter\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
-       "</mark>\n",
-       " exports expanded to \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    60.6  billion baht\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " from \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    56.6 billion\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ".      Export growth was smaller than expected due to lower  earnings from many key commodities including rice whose  earnings declined \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    18 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ", maize \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    66 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ", sugar \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    45 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ", tin \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    26  pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " and canned pineapples \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    seven pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ".      Products registering high export growth were jewellery up  64 pct, clothing \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    57 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       " and rubber \n",
-       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
-       "    35 pct\n",
-       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
-       "</mark>\n",
-       ".  </div></span>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "displacy.render(corpus.loc[\"test/14832\"][\"parsed\"], style='ent', jupyter=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Export corpus Dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "      <th>language</th>\n",
-       "      <th>parsed</th>\n",
-       "      <th>triplets</th>\n",
-       "      <th>keywords</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
-       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
-       "      <td>[(trading, 0.461513063953854), (said, 0.315985...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
-       "      <td>[(VERMIN, (EAT, False), STOCKS), (vermin, (con...</td>\n",
-       "      <td>[(vermin, 0.3120614380287176), (daily, 0.26110...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
-       "      <td>[(JAPAN, (REVISE, False), DEMAND), (Industry, ...</td>\n",
-       "      <td>[(energy, 0.3857636092660117), (demand, 0.3479...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
-       "      <td>[(Products, (registering, False), growth), (Pr...</td>\n",
-       "      <td>[(pct, 0.5457455609144312), (export, 0.2656069...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
-       "      <td>[(INDONESIA, (SEES, False), PRICE), (Indonesia...</td>\n",
-       "      <td>[(indonesia, 0.2410428235502938), (harahap, 0....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label language  \\\n",
-       "id                                                                    \n",
-       "test/14826                                         [trade]       en   \n",
-       "test/14828                                         [grain]       en   \n",
-       "test/14829                                [crude, nat-gas]       en   \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
-       "test/14833                             [palm-oil, veg-oil]       en   \n",
-       "\n",
-       "                                                       parsed  \\\n",
-       "id                                                              \n",
-       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
-       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
-       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
-       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
-       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
-       "\n",
-       "                                                     triplets  \\\n",
-       "id                                                              \n",
-       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...   \n",
-       "test/14828  [(VERMIN, (EAT, False), STOCKS), (vermin, (con...   \n",
-       "test/14829  [(JAPAN, (REVISE, False), DEMAND), (Industry, ...   \n",
-       "test/14832  [(Products, (registering, False), growth), (Pr...   \n",
-       "test/14833  [(INDONESIA, (SEES, False), PRICE), (Indonesia...   \n",
-       "\n",
-       "                                                     keywords  \n",
-       "id                                                             \n",
-       "test/14826  [(trading, 0.461513063953854), (said, 0.315985...  \n",
-       "test/14828  [(vermin, 0.3120614380287176), (daily, 0.26110...  \n",
-       "test/14829  [(energy, 0.3857636092660117), (demand, 0.3479...  \n",
-       "test/14832  [(pct, 0.5457455609144312), (export, 0.2656069...  \n",
-       "test/14833  [(indonesia, 0.2410428235502938), (harahap, 0....  "
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[[\"clean_text\", \"label\", \"language\", \"parsed\"]].to_pickle(\"corpus.p\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Graph Generation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following, we will show you how to create two different kind of graphs out of a corpus of documents:\n",
-    "\n",
-    "* Knowledge base graphs, where the subject-verb-object relation will be encoded to build a semantic graph \n",
-    "* Bipartite graphs, linking documents with the entities/keywords appearing therein"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Knowledge base"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from subject_object_extraction import findSVOs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[\"triplets\"] = corpus[\"parsed\"].apply(lambda x: findSVOs(x, output=\"obj\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "      <th>language</th>\n",
-       "      <th>parsed</th>\n",
-       "      <th>triplets</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
-       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
-       "      <td>[(VERMIN, (EAT, False), STOCKS), (vermin, (con...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
-       "      <td>[(JAPAN, (REVISE, False), DEMAND), (Industry, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
-       "      <td>[(Products, (registering, False), growth), (Pr...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
-       "      <td>[(INDONESIA, (SEES, False), PRICE), (Indonesia...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label language  \\\n",
-       "id                                                                    \n",
-       "test/14826                                         [trade]       en   \n",
-       "test/14828                                         [grain]       en   \n",
-       "test/14829                                [crude, nat-gas]       en   \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
-       "test/14833                             [palm-oil, veg-oil]       en   \n",
-       "\n",
-       "                                                       parsed  \\\n",
-       "id                                                              \n",
-       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
-       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
-       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
-       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
-       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
-       "\n",
-       "                                                     triplets  \n",
-       "id                                                             \n",
-       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...  \n",
-       "test/14828  [(VERMIN, (EAT, False), STOCKS), (vermin, (con...  \n",
-       "test/14829  [(JAPAN, (REVISE, False), DEMAND), (Industry, ...  \n",
-       "test/14832  [(Products, (registering, False), growth), (Pr...  \n",
-       "test/14833  [(INDONESIA, (SEES, False), PRICE), (Indonesia...  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edge_list = [\n",
-    "    {\"id\": _id, \"source\": source.lemma_.lower(), \"target\": target.lemma_.lower(), \"edge\": edge.lemma_.lower()}\n",
-    "    for _id, triplets in corpus[\"triplets\"].iteritems()\n",
-    "    for (source, (edge, neg), target) in triplets\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edges = pd.DataFrame(edge_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "be         7620\n",
-       "have       2675\n",
-       "include    2010\n",
-       "tell       1729\n",
-       "buy        1464\n",
-       "sell       1385\n",
-       "say        1216\n",
-       "take       1172\n",
-       "make       1151\n",
-       "give       1029\n",
-       "Name: edge, dtype: int64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "edges[\"edge\"].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G=nx.from_pandas_edgelist(edges, \"source\", \"target\", \n",
-    "                          edge_attr=True, create_using=nx.MultiDiGraph())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7576"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(G.nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plotDistribution(serie: pd.Series, nbins: int, minValue=None, maxValue=None):\n",
-    "    _minValue=int(np.floor(np.log10(minValue if minValue is not None else serie.min())))\n",
-    "    _maxValue=int(np.ceil(np.log10(maxValue if maxValue is not None else serie.max())))\n",
-    "    bins = [0] + list(np.logspace(_minValue, _maxValue, nbins)) + [np.inf]\n",
-    "    serie.hist(bins=bins)\n",
-    "    plt.xscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def graphSummary(graph, bins=10):\n",
-    "    print(nx.info(graph))\n",
-    "    plt.figure(figsize=(20, 8))\n",
-    "    plt.subplot(1,2,1)\n",
-    "    degrees = pd.Series({k: v for k, v in nx.degree(graph)})\n",
-    "    plt.yscale(\"log\")\n",
-    "    plotDistribution(degrees, bins)\n",
-    "    try:\n",
-    "        plt.subplot(1,2,2)\n",
-    "        allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in graph.edges(data=True)})\n",
-    "        plotDistribution(allEdgesWeights, bins)\n",
-    "        plt.yscale(\"log\")\n",
-    "    except:\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: MultiDiGraph\n",
-      "Number of nodes: 7576\n",
-      "Number of edges: 72263\n",
-      "Average in degree:   9.5384\n",
-      "Average out degree:   9.5384\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(G))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: MultiDiGraph\n",
-      "Number of nodes: 7576\n",
-      "Number of edges: 72263\n",
-      "Average in degree:   9.5384\n",
-      "Average out degree:   9.5384\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "graphSummary(G, bins=15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "np.log10(pd.Series({k: v for k, v in nx.degree(G)}).sort_values(ascending=False)).hist()\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Analysis of a certain relation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>source</th>\n",
-       "      <th>target</th>\n",
-       "      <th>edge</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>test/14826</td>\n",
-       "      <td>exporter</td>\n",
-       "      <td>damage</td>\n",
-       "      <td>fear</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>test/14826</td>\n",
-       "      <td>japan</td>\n",
-       "      <td>fear</td>\n",
-       "      <td>raise</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>test/14826</td>\n",
-       "      <td>row</td>\n",
-       "      <td>damage</td>\n",
-       "      <td>inflict</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>test/14826</td>\n",
-       "      <td>they</td>\n",
-       "      <td>correspondent</td>\n",
-       "      <td>tell</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>test/14826</td>\n",
-       "      <td>they</td>\n",
-       "      <td>u.s.</td>\n",
-       "      <td>tell</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           id    source         target     edge\n",
-       "0  test/14826  exporter         damage     fear\n",
-       "1  test/14826     japan           fear    raise\n",
-       "2  test/14826       row         damage  inflict\n",
-       "3  test/14826      they  correspondent     tell\n",
-       "4  test/14826      they           u.s.     tell"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "edges.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "e = edges[(edges[\"source\"]!=\" \") & (edges[\"target\"]!=\" \") & (edges[\"edge\"]==\"lend\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G=nx.from_pandas_edgelist(e, \"source\", \"target\", \n",
-    "                          edge_attr=True, create_using=nx.MultiDiGraph())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "plt.figure(figsize=(13, 6))\n",
-    "\n",
-    "pos = nx.spring_layout(G, k=1.2) # k regulates the distance between nodes\n",
-    "\n",
-    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos, font_size=12)\n",
-    "\n",
-    "# plt.show()\n",
-    "# plt.savefig(os.path.join(\".\", \"KnowledgeGraph.png\"), dpi=300, format=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bipartite Graph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start by extracting the keywords from the documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gensim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from gensim.summarization import keywords "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('trading', 0.4615130639538529),\n",
-       " ('said', 0.3159855693494515),\n",
-       " ('export', 0.2691553824958079),\n",
-       " ('import', 0.17462010006456888),\n",
-       " ('japanese electronics', 0.1360932626379031),\n",
-       " ('industry', 0.1286043740379779),\n",
-       " ('minister', 0.12229815662000462),\n",
-       " ('japan', 0.11434500812642447),\n",
-       " ('year', 0.10483992409352465)]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text = corpus[\"clean_text\"][0]\n",
-    "keywords(text, words=10, split=True, scores=True, pos_filter=('NN', 'JJ'), lemmatize=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus[\"keywords\"] = corpus[\"clean_text\"].apply(\n",
-    "    lambda text: keywords(text, words=10, split=True, scores=True, pos_filter=('NN', 'JJ'), lemmatize=True)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "      <th>language</th>\n",
-       "      <th>parsed</th>\n",
-       "      <th>triplets</th>\n",
-       "      <th>keywords</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
-       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
-       "      <td>[(trading, 0.461513063953854), (said, 0.315985...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
-       "      <td>[(VERMIN, (EAT, False), STOCKS), (vermin, (con...</td>\n",
-       "      <td>[(vermin, 0.3120614380287176), (daily, 0.26110...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
-       "      <td>[(JAPAN, (REVISE, False), DEMAND), (Industry, ...</td>\n",
-       "      <td>[(energy, 0.3857636092660117), (demand, 0.3479...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
-       "      <td>[(Products, (registering, False), growth), (Pr...</td>\n",
-       "      <td>[(pct, 0.5457455609144312), (export, 0.2656069...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
-       "      <td>[(INDONESIA, (SEES, False), PRICE), (Indonesia...</td>\n",
-       "      <td>[(indonesia, 0.2410428235502938), (harahap, 0....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label language  \\\n",
-       "id                                                                    \n",
-       "test/14826                                         [trade]       en   \n",
-       "test/14828                                         [grain]       en   \n",
-       "test/14829                                [crude, nat-gas]       en   \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
-       "test/14833                             [palm-oil, veg-oil]       en   \n",
-       "\n",
-       "                                                       parsed  \\\n",
-       "id                                                              \n",
-       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
-       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
-       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
-       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
-       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
-       "\n",
-       "                                                     triplets  \\\n",
-       "id                                                              \n",
-       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...   \n",
-       "test/14828  [(VERMIN, (EAT, False), STOCKS), (vermin, (con...   \n",
-       "test/14829  [(JAPAN, (REVISE, False), DEMAND), (Industry, ...   \n",
-       "test/14832  [(Products, (registering, False), growth), (Pr...   \n",
-       "test/14833  [(INDONESIA, (SEES, False), PRICE), (Indonesia...   \n",
-       "\n",
-       "                                                     keywords  \n",
-       "id                                                             \n",
-       "test/14826  [(trading, 0.461513063953854), (said, 0.315985...  \n",
-       "test/14828  [(vermin, 0.3120614380287176), (daily, 0.26110...  \n",
-       "test/14829  [(energy, 0.3857636092660117), (demand, 0.3479...  \n",
-       "test/14832  [(pct, 0.5457455609144312), (export, 0.2656069...  \n",
-       "test/14833  [(indonesia, 0.2410428235502938), (harahap, 0....  "
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def extractEntities(ents, minValue=1, typeFilters=[\"GPE\", \"ORG\", \"PERSON\"]):\n",
-    "    entities = pd.DataFrame([\n",
-    "        {\"lemma\": e.lemma_, \"lower\": e.lemma_.lower(), \"type\": e.label_}\n",
-    "        for e in ents if hasattr(e, \"label_\")\n",
-    "    ])\n",
-    "\n",
-    "    if len(entities)==0:\n",
-    "        return pd.DataFrame()\n",
-    "    \n",
-    "    g = entities.groupby([\"type\", \"lower\"])\n",
-    "\n",
-    "    summary = pd.concat({\n",
-    "        \"alias\": g.apply(lambda x: x[\"lemma\"].unique()), \n",
-    "        \"count\": g[\"lower\"].count()\n",
-    "    }, axis=1)\n",
-    "    \n",
-    "    return summary[summary[\"count\"]>1].loc[pd.IndexSlice[typeFilters, :, :]]\n",
-    "\n",
-    "def getOrEmpty(parsed, _type):\n",
-    "    try:\n",
-    "        return list(parsed.loc[_type][\"count\"].sort_values(ascending=False).to_dict().items())\n",
-    "    except:\n",
-    "        return []\n",
-    "\n",
-    "def toField(ents):\n",
-    "    typeFilters=[\"GPE\", \"ORG\", \"PERSON\"]\n",
-    "    parsed = extractEntities(ents, 1, typeFilters)\n",
-    "    return pd.Series({_type: getOrEmpty(parsed, _type) for _type in typeFilters})\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entities = corpus[\"parsed\"].apply(lambda x: toField(x.ents))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "merged = pd.concat([corpus, entities], axis=1) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>label</th>\n",
-       "      <th>language</th>\n",
-       "      <th>parsed</th>\n",
-       "      <th>triplets</th>\n",
-       "      <th>keywords</th>\n",
-       "      <th>GPE</th>\n",
-       "      <th>ORG</th>\n",
-       "      <th>PERSON</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
-       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
-       "      <td>[(trading, 0.461513063953854), (said, 0.315985...</td>\n",
-       "      <td>[(u.s., 13), (japan, 12), (taiwan, 3), (tokyo,...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
-       "      <td>[(VERMIN, (EAT, False), STOCKS), (vermin, (con...</td>\n",
-       "      <td>[(vermin, 0.3120614380287176), (daily, 0.26110...</td>\n",
-       "      <td>[(china, 2)]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
-       "      <td>[(JAPAN, (REVISE, False), DEMAND), (Industry, ...</td>\n",
-       "      <td>[(energy, 0.3857636092660117), (demand, 0.3479...</td>\n",
-       "      <td>[(japan, 2)]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
-       "      <td>[(Products, (registering, False), growth), (Pr...</td>\n",
-       "      <td>[(pct, 0.5457455609144312), (export, 0.2656069...</td>\n",
-       "      <td>[(thailand, 2)]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>en</td>\n",
-       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
-       "      <td>[(INDONESIA, (SEES, False), PRICE), (Indonesia...</td>\n",
-       "      <td>[(indonesia, 0.2410428235502938), (harahap, 0....</td>\n",
-       "      <td>[(indonesia, 4), (malaysia, 2)]</td>\n",
-       "      <td>[(cpo, 2)]</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                     label language  \\\n",
-       "id                                                                    \n",
-       "test/14826                                         [trade]       en   \n",
-       "test/14828                                         [grain]       en   \n",
-       "test/14829                                [crude, nat-gas]       en   \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
-       "test/14833                             [palm-oil, veg-oil]       en   \n",
-       "\n",
-       "                                                       parsed  \\\n",
-       "id                                                              \n",
-       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
-       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
-       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
-       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
-       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
-       "\n",
-       "                                                     triplets  \\\n",
-       "id                                                              \n",
-       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...   \n",
-       "test/14828  [(VERMIN, (EAT, False), STOCKS), (vermin, (con...   \n",
-       "test/14829  [(JAPAN, (REVISE, False), DEMAND), (Industry, ...   \n",
-       "test/14832  [(Products, (registering, False), growth), (Pr...   \n",
-       "test/14833  [(INDONESIA, (SEES, False), PRICE), (Indonesia...   \n",
-       "\n",
-       "                                                     keywords  \\\n",
-       "id                                                              \n",
-       "test/14826  [(trading, 0.461513063953854), (said, 0.315985...   \n",
-       "test/14828  [(vermin, 0.3120614380287176), (daily, 0.26110...   \n",
-       "test/14829  [(energy, 0.3857636092660117), (demand, 0.3479...   \n",
-       "test/14832  [(pct, 0.5457455609144312), (export, 0.2656069...   \n",
-       "test/14833  [(indonesia, 0.2410428235502938), (harahap, 0....   \n",
-       "\n",
-       "                                                          GPE         ORG  \\\n",
-       "id                                                                          \n",
-       "test/14826  [(u.s., 13), (japan, 12), (taiwan, 3), (tokyo,...          []   \n",
-       "test/14828                                       [(china, 2)]          []   \n",
-       "test/14829                                       [(japan, 2)]          []   \n",
-       "test/14832                                    [(thailand, 2)]          []   \n",
-       "test/14833                    [(indonesia, 4), (malaysia, 2)]  [(cpo, 2)]   \n",
-       "\n",
-       "           PERSON  \n",
-       "id                 \n",
-       "test/14826     []  \n",
-       "test/14828     []  \n",
-       "test/14829     []  \n",
-       "test/14832     []  \n",
-       "test/14833     []  "
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We finally create the bipartite graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edges = pd.DataFrame([\n",
-    "    {\"source\": _id, \"target\": keyword, \"weight\": score, \"type\": _type}\n",
-    "    for _id, row in merged.iterrows()\n",
-    "    for _type in [\"keywords\", \"GPE\", \"ORG\", \"PERSON\"] \n",
-    "    for (keyword, score) in row[_type]\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = nx.Graph()\n",
-    "G.add_nodes_from(edges[\"source\"].unique(), bipartite=0)\n",
-    "G.add_nodes_from(edges[\"target\"].unique(), bipartite=1)\n",
-    "G.add_edges_from([\n",
-    "    (row[\"source\"], row[\"target\"])\n",
-    "    for _, row in edges.iterrows()\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "document_nodes = {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 0}\n",
-    "entity_nodes = {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 1}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes_with_low_degree = {n for n, d in nx.degree(G, nbunch=entity_nodes) if d<5}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 25752\n",
-      "Number of edges: 100311\n",
-      "Average degree:   7.7905\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(G))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from networkx.algorithms.bipartite.projection import overlap_weighted_projected_graph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Entity-Entity Graph Projection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subGraph = G.subgraph(set(G.nodes) - nodes_with_low_degree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entityGraph = overlap_weighted_projected_graph(\n",
-    "    subGraph, \n",
-    "    {n for n, d in subGraph.nodes(data=True) if d[\"bipartite\"] == 1}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2386"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(entityGraph.nodes())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = pd.Series({k: v for k, v in nx.degree(entityGraph)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(degrees, 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 2386\n",
-      "Number of edges: 120198\n",
-      "Average degree: 100.7527\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(entityGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in entityGraph.edges(data=True)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(allEdgesWeights, 100)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filteredEntityGraph = entityGraph.edge_subgraph(\n",
-    "    [edge for edge in entityGraph.edges if entityGraph.edges[edge][\"weight\"]>0.05]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 2265\n",
-      "Number of edges: 8082\n",
-      "Average degree:   7.1364\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(filteredEntityGraph))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Local and global properties of the graph "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "globalKpis = [{\n",
-    "    \"shortest_path\": nx.average_shortest_path_length(_graph),\n",
-    "    \"clustering_coefficient\": nx.average_clustering(_graph),\n",
-    "    \"global_efficiency\": nx.global_efficiency(_graph)\n",
-    "} for components in nx.connected_components(filteredEntityGraph) \n",
-    "    for _graph in [nx.subgraph(filteredEntityGraph, components)]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>shortest_path</th>\n",
-       "      <th>clustering_coefficient</th>\n",
-       "      <th>global_efficiency</th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4.715074</td>\n",
-       "      <td>0.211563</td>\n",
-       "      <td>0.227356</td>\n",
-       "      <td>2254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.333333</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   shortest_path  clustering_coefficient  global_efficiency     0\n",
-       "0       4.715074                0.211563           0.227356  2254\n",
-       "1       1.000000                0.000000           1.000000     2\n",
-       "2       1.500000                0.000000           0.750000     4\n",
-       "3       1.333333                0.000000           0.833333     3\n",
-       "4       1.000000                0.000000           1.000000     2"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([\n",
-    "    pd.DataFrame(globalKpis), \n",
-    "    pd.Series([len(c) for c in nx.connected_components(filteredEntityGraph)])\n",
-    "], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2265"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series([len(c) for c in nx.connected_components(filteredEntityGraph)]).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'shortest_path': 4.715073779178782,\n",
-       " 'clustering_coefficient': 0.21156314975836948,\n",
-       " 'global_efficiency': 0.2273555107741054}"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "globalKpis[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# nx.write_gexf(filteredEntityGraph, \"filteredEntityGraph.gexf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "betweeness = nx.betweenness_centrality(filteredEntityGraph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_betweeness = pd.Series(betweeness)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(_betweeness[_betweeness>0], 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pageRanks = pd.Series(nx.pagerank(filteredEntityGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = pd.Series({k: v for k, v in nx.degree(filteredEntityGraph)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpis = pd.concat({\n",
-    "    \"pageRank\": pageRanks, \n",
-    "    \"degrees\": degrees, \n",
-    "    \"betweeness\": _betweeness\n",
-    "}, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1e-05, 0.02)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.title(\"Page rank vs degrees\")\n",
-    "plt.plot(kpis[\"pageRank\"], kpis[\"degrees\"], '.', color=\"tab:blue\")\n",
-    "plt.xlabel(\"page rank\")\n",
-    "plt.ylabel(\"degree\")\n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.title(\"Page rank vs betweeness\")\n",
-    "plt.plot(kpis[\"pageRank\"], kpis[\"betweeness\"], '.', color=\"tab:blue\")\n",
-    "plt.xlabel(\"page rank\")\n",
-    "plt.ylabel(\"betweeness\")\n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "plt.ylim([1E-5, 2E-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(6,4))\n",
-    "plt.plot(kpis[\"pageRank\"], kpis[\"betweeness\"], 'b.')\n",
-    "plt.xlabel(\"page rank\")\n",
-    "plt.ylabel(\"betweeness\")\n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Edge Weight Distribution')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAFDCAYAAAAqKoi3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfdElEQVR4nO3df7RdZX3n8c9HUjQJeBESQxMIl/QyLKOZGboiiG3t7RRpMF6DrT/IpDooTYprQscptgSlwHSJhE6pJcqUBqFp0QJZVDExYbDOePlVHPkxdiAgyxiDSUCRX1fyo2LgO3/sfcrO4dzc59x7zt37nPN+rXVXcvfZZ+/vOXvf53zOc579HEeEAAAAABzca8ouAAAAAOgEBGcAAAAgAcEZAAAASEBwBgAAABIQnAEAAIAEBGcAAAAgAcEZXc32btvzWrStT9r+Qv7/ftthe0qLtj03r/WQVmwPQGdrdRvTDraX2f564rpn2757EmraYnuwRds64PHlx2OgFdvOt9ey1ydMHoJzD7O93fY+2y/Yft72P9k+13blzwvbg7Zfzhue3bZ32l5v+63F9SLisIjYlrCtnWPtMyI+ExG/N9Ha831ut31aYds/zGt9qRXbB1A9hTZ3d+Hn85Ncw4W2b6tb9r1Rlp11sG1FxJci4vQW1TVse9T2tfBGova8/dj212y/s66mN0fE8Bj7SnpT0u7Hl/L6hOqpfEBC2w1FxOGSjpO0WtIFkq5rx47a0Jv6REQcJulwSW+T9F1Jd9n+zRbvR1Xu9QHQUYbywFT7WTnJ+79T0ttr7bHtX5T0C5JOqls2kK9bNUfk7f6/k/SPkr5i++xW74Q2H6MhOEOSFBEjEbFB0gcl/Sfbb5Ek26+1/ee2f5i/w7/G9tTa/Wz/se0nbT9h+/eKH2XZXmf7r2xvtr1H0m/Ynm37H2z/xPYPbP9BYVuvsb3K9vdtP5P3IB+ZUHtExM6IuFjSFyRdUdhmsZ532X4k72HfZfsTtqdLuk3S7EJPxmzbl9q+xfYXbf9U0tn5si/W7f6j+WN/0vYnCvtdZ/vThd//tVfb9g2S5kramO/vj+t7QPIaNth+1vZW28sL27o0f27+Ln8sW2wvHOt5AlBdtg/J29qnbW+TtLju9uNt35n/zX/D9tXF9sj225x9avi87X/26MMV7lMWlP99/vuvSfqmpMfqln0/Ip6w3Wf7uryN22X704WAfcDwC9un237M9ojt/2H7jvpe1vwxPpe3/2fkyy7L9/l5J/bCR8SPIuIqSZdKusL5J6UufJpn+2Tb99v+af769Rf53WtvCJ7P93dq/ljusf1Z289IurT+8eXeZXtbfpz+e2G/B7w+FNv00R5f3etTX96m/8T247YvKmz7bNt3N3ruMPkIzjhARHxb0k5lf+RS1gv9b5Q1qAOS5ki6WJJsL5L0h5JOy28bbLDJ/yjpMmW9wv8kaaOkf86385uSPm77t/J1z5N0pqRflzRb0nOSrm7yIXxZ0i/ngbjedZJ+P+9hf4uk/x0ReySdobz3Ov95Il9/iaRbJB0h6Uuj7O83JJ0g6XRJF7gw/GI0EfEhST/UKz1Pf9ZgtZuUHYfZkt4n6TO2/0Ph9vfk6xwhaYOkSf24F0DLLZf0bkknSVqo7O++6O8lfVvSUcrC4odqN9ieI2mTpE9LOlLSJyT9g+2Z9TuJiBcl/R9J78gXvUPSXZLurltWC5frJO1X1safpKyte9WQCtszlLWXF+Y1Pibp7XWrnZIvnyHpzyRdZ9sR8am8hpXj6IX/sqQ3SjqxwW1XSboqIl4v6ZckrS88PinvvY6Iewv1bZM0S9nrViPvVXZ8flnZa8RHxyow8fF9TlKfpHnKXgM/LOkjhdsbPndj7RutR3BGI09IOjL/o1wh6b9GxLMR8YKkz0iqjXv7gKS/iYgtEbFXWWNe76sRcU9EvCxpgaSZEfGnEfFiPrbr2sL2zpX0qbz3+Gf59t7n5j4ye0KSlQXKej+XNN/26yPiuYh4cIxt3RsRt0bEyxGxb5R1/ltE7ImIhyT9jaSlTdTakO1jJf2KpAsi4l8i4jvKetI/XFjt7ojYnI+JvkHZx5YAqu/WvFe49lP7NOkDkv4yInZExLOSLq/dwfZcSW+VdHHedt6t7A1zze9K2py3CS9HxD9Kul/Su0ap4Q69Eh5/TVmou6tu2R22Z+Xb+Hjezj0l6bN6pc0uepekLRHx5YjYL2mNpB/VrfN4RFybt1t/K+kXlYXUiah1dDT6dPLnkgZsz4iI3RHxrbG2FRGfi4j9B2nzr8hfD38o6S/Vmjb/EGXP6YUR8UJEbJd0pQpvjtSe5w7jQHBGI3MkPStppqRpkh6oNfKS/me+XMp6Q3cU7lf8f6NlxykbEvF8YXuf1Ct//McpG69Wu+1RSS+pucZhjqSQ9HyD235HWeP+eP4R4qljbKvR4znYOo8re04marak2huV4rbnFH4vviDtlfS6Jt9gACjHmRFxROHn2nx5fXv6eOH/tTZhb2FZfdv6/rq29VeVhatG7pT0q86Gws2MiO8p+0Tw7fmyt+TrHKdsWMeThe3+tbIe3noH1B8RoexTs6IfFW6vPZbDRqkxVa1dfLbBbeco+8T0u7bvs/3uMbZVVps/Q9nzXDzmo7b5LXzuMA680OIAzmalmKPsY7unJe2T9OaI2NVg9SclHVP4/dgG60Th/zsk/SAiThhl9zskfTQi7mm68Fe8V9KD+RCMAwuJuE/SEtu/IGmlso/tjq2r8YC7JOzvWGUXJUrZuOVa78ceZW86ao5uYtu1Hv/DC+F5rqRGxwBAd3hSB7ahc+tuO9L2tEJoKq67Q9INEbFcae5VNixguaR7JCkifmr7iXzZExHxA9v/Iulnkmbkvchj1f+vrwf5J5bHjL76q6S0t428V9JTyoYxHLjB7A3B0nys8G9LusX2UQfZV2qbvyX/f6va/KeV9Y4fJ+mRwrZp8yuIHmdIkmy/Pn83fpOkL0bEQ/nwimslfdb2G/P15hTGJK+X9BHbb7I9TdKfjLGbb0t6wfYFtqc6uxjmLX5lCrlrJF1m+7h8XzNtL0mo3Xldlygbe/fJBusc6mxOzr6I+Lmkn0p6Ob/5x5KOst031r4a+BPb02y/Wdl4tJvz5d9RdhHJkbaPlvTxuvv9WNlYtleJiB3Ken8ut/062/9WWc9J/YWJALrHekl/YPsY22+QtKp2Q0Q8rmzoxaV5W3aqpKHCfb8oacj2b+Xt6uucXZDcMLjmwxDuV3aNyl2Fm+7Ol92Zr/ekpK9LujJ/jXiN7V+y/esNNrtJ0gLbZ+affv1nvTo8HsyobWIjtmfZXinpEmVDHF5usM7v2p6Z3/Z8vvhlST/J/x3PHMp/ZPsN+ZC6/6ID2/x3OJuTv0/ZWO+ig7X5Lyk7/pfZPjx/DfxD0eZXEsEZG22/oKzH4lOS/kIHXpBwgaStkr7lbHaJbyi/CCMiblM2ju2btXXy+/ys0Y7yxuHdyi40/IGyd9lfUNbzIWUXcmyQ9PW8pm8puyBiNLNt75a0W9mV4gskDUbEaBPyf0jS9vxxnCtpWV7XdyXdKGlb/nFkMx+93aHssf8vSX9e2PcNyi6C3K7shefmuvtdLumifH+f0KstldSvrDfjK5IuiYhvNFEXgGqqzaZT+/lKvvxaSbcrazceVHbRW9EySadKekbZRYA3K29r8zfbS5R1GvxEWXv+Rzr4a/wdyoZcFGeNuCtfVpyG7sOSDlXWE/qcsgsAXzUEJCKelvR+ZReuPSNpvrJw3vD1oIGrlF3T8pztNQdZ73lnszQ9pGzo3fsj4vpR1l0kaUv+OnGVpLMiYl/ea3+ZpHvyNvhtiTVK0lclPaAsKG9SPn1rPq78Zkn/L7/9a00+vvOU9VpvU3ZM/l7SaI8LJXI2DAmYONtvkvSwpNcmfKwHABgn2zdL+m5EXFJ2LY3kwyN2SloWEd8sux6gVehxxoTYfq+zuZ7foGz+5I2EZgBoLdtvzYdJvMbZVKBLJN1aclkHyIeKHGH7tcp6v61XPokEugLBGRP1+8ouzPi+shkwPlZuOQDQlY6WNKxsaNoaSR+LiP9bakWvdqqy14KnlY3BPvMg07oBHYmhGgAAAEACepwBAACABARnAAAAIEElvgBlxowZ0d/f3/T99uzZo+nTp7e+IHQVzhOkGO958sADDzwdETPHXrN7HHHEETEwMFB2GahDW1c9HJPqmWibXYng3N/fr/vvv7/p+w0PD2twcLD1BaGrcJ4gxXjPE9uPj71Wd7A9JGlo9uzZ42qz0V60ddXDMameibbZDNUAACSJiI0RseKwww4ruxQAKAXBGQAAAEhAcAYAAAASlBqcbQ/ZXjsyMlJmGQAAAMCYSg3OtfFyfX19ZZYBAAAAjImhGgAAAEACgjMAAACQgOAMAAAAJCA4AwAAAAkIzgAAAEACpqMDAAAAEkwpc+cRsVHSxoULFy4vsw6Ur3/VprZte92i6W3bNtCL9v38peS/2e2rF7e5GgCYPAzVAAAAABIQnAEASWrD6/bt3VN2KQBQilKHagAAOkdteN3ceQPJw+vaMQyL4R8AykKPMwAAAJCA4AwAAAAkIDgDAAAACZjHGQAAAEhQanCOiI0RsaKvr6/MMgAAAIAxMVQDAAAASEBwBgAAABIQnAEAAIAEBGcAAAAgAcEZAAAASEBwBgAAABJMKbsAAACa0b9qU8u3uX314pZvE0D3occZAAAASMA3BwIAAAAJ+OZAAAAAIAFDNQAAAIAEBGcAAAAgAcEZAAAASEBwBgAAABIwjzO63kO7RnR2G+Z9lZj7Fd3B9nRJd0i6NCK+VnY9AFBV9DgDQJexfb3tp2w/XLd8ke3HbG+1vapw0wWS1k9ulQDQeQjOANB91klaVFxg+xBJV0s6Q9J8SUttz7f9TkmPSHpqsosEgE7DUA0ka8fX3AJovYi403Z/3eKTJW2NiG2SZPsmSUskHSZpurIwvc/25oh4eTLrBYBOQXAGgN4wR9KOwu87JZ0SESslyfbZkp4eLTTbXiFphSTNmDFTFy/Y395qJ9nw8HDZJUzY7t27u+JxdBOOSfcpNTjbHpI0NDAwUGYZANDzImLdGLevlbRWkubOG4grH+qufpftywbLLmHChoeHNTg4WHYZKOCYdB++chsAesMuSccWfj8mXwYASMTFgQDQG+6TdILt420fKuksSRua2YDtIdtr9+3d05YCAaDqCM4A0GVs3yjpXkkn2t5p+5yI2C9ppaTbJT0qaX1EbGlmu7VPCadOm976ogGgA3TXIDUAgCJi6SjLN0vaPMnldIR2zRrElyQB3YUeZwAAACABwRkAAABIQHAGACTh4kAAvY7gDABIwsWBAHodwRkAAABIQHAGAAAAEhCcAQBJGOMMoNcRnAEASRjjDKDXEZwBAACABARnAAAAIEGpwbk2Xm5kZKTMMgAAAIAxlRqca+Pl+vr6yiwDAAAAGBNDNQAASZhVA0CvIzgDAJIwqwaAXkdwBgAAABIQnAEAAIAEBGcAAAAgAcEZAAAASEBwBgAkYVYNAL2O4AwASMKsGgB63ZSyCwAAoFv1r9rU8m1uX7245dsEkIYeZwAAACABwRkAAABIQHAGAAAAEhCcAQAAgAQEZwAAACABwRkAkIR5nAH0OoIzACAJ8zgD6HUEZwAAACABwRkAAABIQHAGAAAAEpQanGsXmoyMjJRZBgAAADCmUoNz7UKTvr6+MssAAAAAxsRQDQAAACABwRkAAABIMKXsAtBa/as2lV0CAABAV6LHGQCQhG8OBNDrCM4AgCR8cyCAXkdwBgAAABIwxrkEjEMGAADoPPQ4AwAAAAkIzgAAAEACgjMAAACQgOAMAAAAJCA4AwAAAAkIzgAAAEACgjMAAACQgOAMAAAAJCA4AwAAAAkIzgAAAEACgjMAIIntIdtr9+3dU3YpAFAKgjMAIElEbIyIFVOnTS+7FAAoxZSyC6iq/lWbyi4BAAAAFUKPMwAAAJCA4AwAAAAkYKgGAAAdZLShhOcv2K+zJzDMcPvqxeO+L9Ar6HEGAAAAEhCcAQAAgAQEZwAAACBBR49xfmjXyITGcwEAAACp6HEGAAAAEhCcAQAAgAQEZwAAACBBy8c42z5T0mJJr5d0XUR8vdX7AAAAACZbUo+z7ettP2X74brli2w/Znur7VWSFBG3RsRySedK+mDrSwYAAAAmX+pQjXWSFhUX2D5E0tWSzpA0X9JS2/MLq1yU3w4AAAB0vKTgHBF3Snq2bvHJkrZGxLaIeFHSTZKWOHOFpNsi4sHWlgsAAACUYyJjnOdI2lH4faekUySdJ+k0SX22ByLimkZ3tr1C0gpJmjVrloaHh5suYNZU6fwF+5u+H3pLO8+T8Zy3qKbdu3dzPAEAB9XyiwMjYo2kNQnrrZW0VpIWLlwYg4ODTe/rc1/6qq58qKO/wwWT4PwF+9t2nmxfNtiW7WLyDQ8PazztEACgd0xkOrpdko4t/H5MvgwAAADoOhMJzvdJOsH28bYPlXSWpA2tKQsAMBlsv8n2NbZvsf2xsusBgCpLnY7uRkn3SjrR9k7b50TEfkkrJd0u6VFJ6yNiS/tKBQCkaHIK0Ucj4lxJH5D0K2XUCwCdImngZ0QsHWX5ZkmbW1oRAGCi1kn6vKS/qy0oTCH6TmUXc99ne0NEPGL7PZI+JumGEmoFgI5R6ldu2x6yvXZkZKTMMgCgqzQzhWi+/oaIOEPSssmtFAA6S6nBOSI2RsSKvr6+MssAgF7QaArRObYHba+x/dfiE0QAOCjmcgOAHhYRw5KGx1qvOPf+jBkzdTFz6FfOROesZx7z1mN++O5DcAaA3jChKUSLc+/PnTcQzKFfPROds5556VuP+eG7T6lDNQAAk4YpRAFggugyACagf9Wmtm17++rFbds2uls+heigpBm2d0q6JCKus12bQvQQSdc3O4Wo7SFJQzNmHa3prS4aADpAqcG51ggPDAyUWQYAdJV2TSEaERslbZw7b2D5eLcBAJ2MWTUAAACABIxxBgAAABIQnAEASWpfWrVv756ySwGAUhCcAQBJasPrpk7j0kAAvYngDAAAACQgOAMAAAAJSg3OtfFyIyMjZZYBAAAAjInp6AAASbg4EECvY6gGACAJFwcC6HUEZwAAACABwRkAAABIQHAGAAAAEhCcAQAAgAQEZwBAEmbVANDrmMcZAJCEWTUA9DrmcQYAAAASMFQDAAAASEBwBgAAABIQnAEAAIAEBGcAQBJm1QDQ6wjOAIAkzKoBoNcRnAEAAIAEU8ouAAAAlK9/1aaWb3P76sUt3yZQJnqcAQAAgAR8cyAAAACQgG8OBAAAABIwVAMAAABIQHAGACRhHmcAvY7gDABIwjzOAHodwRkAAABIQHAGAAAAEhCcAQAAgAQEZwAAACABwRkAAABIQHAGAAAAEvCV2wAAAEACvnIbAAAASMBQDQBAEr45EECvIzgDAJLwzYEAeh3BGQAAAEhAcAYAAAASEJwBAACABARnAAAAIAHBGQAAAEhAcAYAAAASEJwBAACABARnAAAAIAHBGQAAAEhAcAYAAAASEJwBAACABKUGZ9tDtteOjIyUWQYAAAAwplKDc0RsjIgVfX19ZZYBAEhQ6+zYt3dP2aUAQCkYqgEASFLr7Jg6bXrZpQBAKQjOAAAAQIIpZRcAAAC6U/+qTS3f5vbVi1u+TSAVPc4AAABAAoIzAAAAkIDgDAAAACQgOAMAAAAJCM4AAABAAoIzAAAAkIDgDAAAACQgOAMAAAAJCM4AAABAAoIzAAAAkIDgDAAAACQgOAMAAAAJCM4AAABAAoIzAAAAkIDgDAAAACQgOAMAAAAJSg3Otodsrx0ZGSmzDADoabbPtH2t7Zttn152PQBQVaUG54jYGBEr+vr6yiwDALqO7ettP2X74brli2w/Znur7VWSFBG3RsRySedK+mAZ9QJAJ2CoBgB0p3WSFhUX2D5E0tWSzpA0X9JS2/MLq1yU3w4AaIDgDABdKCLulPRs3eKTJW2NiG0R8aKkmyQtceYKSbdFxIOTXSsAdIopZRcAAJg0cyTtKPy+U9Ipks6TdJqkPtsDEXFN/R1tr5C0QpJmzJipixfsn4Ry0YxZU6Xze+C4DA8Pl11Cst27d3dUvRgbwRkAelxErJG0Zox11kpaK0lz5w3ElQ/x8lE15y/Yr144LtuXDZZdQrLh4WENDg6WXQZaiKEaANA7dkk6tvD7MfkyAEACgjMA9I77JJ1g+3jbh0o6S9KGkmsCgI5BcAaALmT7Rkn3SjrR9k7b50TEfkkrJd0u6VFJ6yNiSxPbHLK9dt/ePe0pGgAqrvsHQwFAD4qIpaMs3yxp8zi3uVHSxrnzBpZPpDYA6FT0OAMAAAAJCM4AAABAAoIzACAJY5wB9DqCMwAgSURsjIgVU6dNL7sUACgFwRkAAABIQHAGAAAAEhCcAQAAgATM4wwASGJ7SNLQjFlHi1HOKEv/qk1t2e721Yvbsl10F3qcAQBJuDgQQK8jOAMAAAAJCM4AAABAAoIzAAAAkIDgDABIwjcHAuh1BGcAQBIuDgTQ6wjOAAAAQAKCMwAAAJCA4AwAAAAkIDgDAAAACQjOAAAAQAKCMwAgCdPRAeh1BGcAQBKmowPQ6wjOAAAAQIKWB2fb82xfZ/uWVm8bAAAAKEtScLZ9ve2nbD9ct3yR7cdsb7W9SpIiYltEnNOOYgEAAICypPY4r5O0qLjA9iGSrpZ0hqT5kpbant/S6gAAAICKSArOEXGnpGfrFp8saWvew/yipJskLWlxfQAAAEAlTJnAfedI2lH4faekU2wfJekySSfZvjAiLm90Z9srJK2QpFmzZml4eLjpAmZNlc5fsL/p+6G3dOp5Mp6/CYzf7t27ec4BAAc1keDcUEQ8I+nchPXWSlorSQsXLozBwcGm9/W5L31VVz7U8oeALnP+gv0deZ5sXzZYdgk9ZXh4WONph3qJ7SFJQzNmHS0mpAPQiyYyq8YuSccWfj8mXwYA6ELM4wyg100kON8n6QTbx9s+VNJZkja0piwAAACgWlKno7tR0r2STrS90/Y5EbFf0kpJt0t6VNL6iNjSvlIBAACA8iQN/IyIpaMs3yxpc0srAgAAACqo1K/ctj1ke+3IyEiZZQAAAABjKjU41y406evrK7MMAAAAYEylBmcAAACgUxCcAQAAgAQEZwAAACABwRkAkKR2Qfe+vXvKLgUASsGsGgCAJHxzIIBex6waAAAAQAKGagAAAAAJCM4AAABAAoIzAAAAkIDgDAAAACQgOAMAAAAJmI4OAAAASMB0dAAAAEAChmoAAAAACQjOAAAAQAKCMwAAAJCA4AwAAAAkIDgDAJLUZkLat3dP2aUAQCkIzgCAJLWZkKZOm152KQBQiill7tz2kKShgYGBMssAKql/1aa2bXv76sVt2zYAdKJ2tLnrFvEms9swjzMAAACQgKEaAAAAQAKCMwAAAJCA4AwAAAAkIDgDAAAACQjOAAAAQAKCMwAAAJCA4AwAAAAkKDU4176+dWRkpMwyAAAAgDHxBSgAAABAAoZqAAAAAAkcEWXXINsjkr43ys19kkYbyzFD0tNtKap1DlZ/lfYxnm00c5/Udcda72C3j3ZbJ5wnUvvPlbLOk2bvl7Julc6T4yJi5jju17FsvyDpsZJ2X/X2rh3nZqPljZaV1da1qu1q13HpxWMi8bcy2rITI+LwhPoai4jSfyStHedt95dd+0QeW5X2MZ5tNHOf1HXHWm8850onnCetOo7t3v54t9Hqc6WXz5Mq/JT5XFW9vWvTufmq5aMsK+W4tKrtatdx6cVj0qrj0o1/KxM9JlUZqrFxnLd1gsmovxX7GM82mrlP6rpjrce5Uu72x7uNVp8rvXye9Lqqt3ftODcbLa/SedyqWtp1XHrxmEj8raTupymVGKoxXrbvj4iFZdeBauM8QQrOk3Q8V9XEcakejkn1TPSYVKXHebzWll0AOgLnCVJwnqTjuaomjkv1cEyqZ0LHpKN7nAEAAIDJ0uk9zgAAAMCkIDgDAAAACQjOAAAAQIKuCc62p9v+W9vX2l5Wdj2oLtvzbF9n+5aya0F12T4zb09utn162fV0Cp636qHNqw6ySvU0+/dR6eBs+3rbT9l+uG75ItuP2d5qe1W++Lcl3RIRyyW9Z9KLRamaOVciYltEnFNOpShTk+fJrXl7cq6kD5ZR72Rrss1tqBeft3Zq0TGhzWsjskr1tDMTVDo4S1onaVFxge1DJF0t6QxJ8yUttT1f0jGSduSrvTSJNaIa1in9XEHvWqfmz5OL8tt7wTolPj+2F9j+Wt3PGwt37aXnrZ3WqXXHBO2xTmSVqlmnNmWCKa2orl0i4k7b/XWLT5a0NSK2SZLtmyQtkbRT2Qn5HVX/DQFarMlz5ZFJLg8V0cx5YvtRSasl3RYRD05upeVo5vmJiMslvbt+G7atHnve2qkVxwTtRVapnnZmgk48aHP0yrs1KTsJ50j6sqTfsf1Xqt7XXqIcDc8V20fZvkbSSbYvLKc0VMhobcp5kk6T9D7b55ZRWEWM9vyMhuet/Zo6JrR5pSCrVE9LMkGle5ybERF7JH2k7DpQfRHxjLLxl8CoImKNpDVl19FpeN6qhzavOsgq1dPs30cn9jjvknRs4fdj8mVAPc4VpOA8OTien+rhmFQfx6h6WnJMOjE43yfpBNvH2z5U0lmSNpRcE6qJcwUpOE8Ojuenejgm1ccxqp6WHJNKB2fbN0q6V9KJtnfaPici9ktaKel2SY9KWh8RW8qsE+XjXEEKzpOD4/mpHo5J9XGMqqedx8QR0dpqAQAAgC5U6R5nAAAAoCoIzgAAAEACgjMAAACQgOAMAAAAJCA4AwAAAAkIzgAAAEACgjMAAACQgOAMAAAAJCA4AwAAAAn+P1mPY6PsO8YwAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plotDistribution(degrees, 13)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Degree Distribution\")\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plotDistribution(allEdgesWeights, 20)\n",
-    "plt.xlim([1E-2, 10])\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Edge Weight Distribution\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in filteredEntityGraph.edges(data=True)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(allEdgesWeights, 20)\n",
-    "plt.xlim([1E-2, 10])\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Visualization of the Network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Create network layout for visualizations\n",
-    "spring_pos = nx.spring_layout(filteredEntityGraph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.axis(\"off\")\n",
-    "nx.draw_networkx(filteredEntityGraph, pos=spring_pos, node_color=default_node_color, \n",
-    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Community detection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities = pd.Series(community.best_partition(filteredEntityGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '# Members')"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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vchKw3yBykyRJkno1qJHpzwJHAY/V1zcGfp+Zj9TXbwM2H+uGETEvIhZFxKIVK1b0PVFJkiRpPJNeTEfEq4G7M/Oqkttn5sLMnJuZc0dGRlrOTpIkSerdrAHc5y7AayJib+ApwHrA54ANImJWPTq9BXD7AHKTJEkDMmf+uT1tt3zBPn3OROrdpI9MZ+Z7M3OLzJwDHAhclJlvBC4G9q83Oxg4e7JzkyRJkpoYpuNMvwd4V0Qso5pDfdyA85EkSZJWahDTPP4iMy8BLqmXbwJ2GmQ+kiRJUhPDNDItSZIkTSkW05IkSVIhi2lJkiSpkMW0JEmSVMhiWpIkSSpkMS1JkiQVspiWJEmSCllMS5IkSYUspiVJkqRCFtOSJElSIYtpSZIkqZDFtCRJklTIYlqSJEkqZDEtSZIkFbKYliRJkgpZTEuSJEmFLKYlSZKkQhbTkiRJUiGLaUmSJKmQxbQkSZJUyGJakiRJKmQxLUmSJBWaNegEJEmS+mHO/HNXuc3yBftMQiaazhyZliRJkgpZTEuSJEmFLKYlSZKkQhbTkiRJUiGLaUmSJKmQxbQkSZJUyGJakiRJKjTpxXREbBkRF0fEzyPihog4om7fKCIuiIhf1X83nOzcJEmSpCYGMTL9CPDuzNwe2Bl4e0RsD8wHLszMbYAL6+uSJEnS0Jr0Yjoz78jMxfXyH4GlwObAvsBJ9WYnAftNdm6SJElSEwOdMx0Rc4AXAZcDm2bmHfWqO4FNB5WXJEmS1ItZg7rjiFgH+CbwPzPzvoj4y7rMzIjIcW43D5gHMHv27MlIVZIkzXBz5p+7ym2WL9hnEjLRsBnIyHRErEFVSJ+cmd+qm++KiM3q9ZsBd49128xcmJlzM3PuyMjI5CQsSZIkjWHSR6ajGoI+DliamZ/uWHUOcDCwoP579mTnJkmS1G+Ock8vg5jmsQvwJmBJRFxTt72Pqog+IyIOA24G3jCA3CRJkqSeTXoxnZk/AWKc1XtMZi6SJEnSRHgGREmSJKmQxbQkSZJUyGJakiRJKmQxLUmSJBUa2ElbJEmSNDEeZm/wHJmWJEmSCllMS5IkSYUspiVJkqRCFtOSJElSIYtpSZIkqZDFtCRJklTIYlqSJEkqZDEtSZIkFbKYliRJkgpZTEuSJEmFLKYlSZKkQhbTkiRJUiGLaUmSJKmQxbQkSZJUyGJakiRJKmQxLUmSJBWymJYkSZIKWUxLkiRJhSymJUmSpEIW05IkSVIhi2lJkiSp0KxBJyBJkqTBmzP/3J62W75gnz5nMrU4Mi1JkiQVcmRakiRJrZpJo9yOTEuSJEmFHJmWJEnS0GpzlLsfI+ZDNzIdEXtFxI0RsSwi5g86H0mSJGk8Q1VMR8TqwBeAVwHbAwdFxPaDzUqSJEka21AV08BOwLLMvCkzHwJOA/YdcE6SJEnSmCIzB53DX0TE/sBemfmP9fU3AS/JzHd0bDMPmFdffTZwYw+hNwHuaSlNYw0uVtvxjGWsfscz1vSI1XY8Yxmr3/GM1Z9Yz8jMke7GKfcDxMxcCCxscpuIWJSZc9u4f2MNLlbb8YxlrH7HM9b0iNV2PGMZq9/xjDW5sYZtmsftwJYd17eo2yRJkqShM2zF9JXANhGxVUSsCRwInDPgnCRJkqQxDdU0j8x8JCLeAfwAWB04PjNvaCF0o2khxhraWG3HM5ax+h3PWNMjVtvxjGWsfscz1iTGGqofIEqSJElTybBN85AkSZKmDItpSZIkqZDFtCRJklRoqH6AqN5ExEuApZl5X0Q8FZgPvBj4OfCxzPxDw3g7AZmZV9anb98L+EVmfq8gt+2AzYHLM/NPHe17Zeb3m8ZrQ8eRYf4jM38YEf8AvBRYCizMzIcHkVdHfttRnelz87rpduCczFw6uKyeLCK+mplvbiHOy6jOdnp9Zp4/8cy0MhFxOHBWZt466FwkVSLimcDrqA4H/CjwS+CUzLxvoImpiD9AHKCI2Dgzf1twuxuAF9ZHP1kI3A+cCexRt7+uQayjgVdRfbC6AHgJcDHwX4AfZOZHG8Q6HHg7VZG6A3BEZp5dr1ucmS/uNVabIuJkqse3NvB7YB3gW1T7KzLz4EHkVef2HuAg4DTgtrp5C6ri/7TMXDCgvLoPSRnAK4CLADLzNQ1iXZGZO9XL/52qj5wF/D3wnUE9xpkiIv4A/Bn4NXAq8I3MXNFi/L/OzLvbitcPEXFoZp7Q47azgMOA1wJPr5tvB84Gjhv0h29NffV75auBS4G9gaup3pteC/yPzLxkgLmtDbwDSOD/Ur0XvQ74BfDhzkGywvi/zMxtJ5zosMnMGXMBzmu4/frAAqpOdC/wW6pCcQGwQcNYC4BN6uW5wE3AMuBmYNeGsZZ2LC/uWndNw1hLqA5DuDZwH7Be3f5U4LqCWOvUy3OARVQFNcDVDWOtA3wYuAH4A7ACuAw4pOB5v67+Owu4C1i9vh5NH2Mf+uQvgTXGaF8T+FXDWHt1LK8PHAdcB5wCbNow1mLg68BuwK713zvq5ab99eqO5SuBkXr5r4AlA9z3TwOOBb4AbAx8qO7DZwCbDbJftPw4r6aa0vf3dZ9YAXwfOBhYt2GsjbouGwPLgQ2BjQb9WFeS9y0Ntj217hc7U32w3aJePhY4veH9rgd8HPga8A9d67446P0yRr5/3WKsjQtvN5dqQOfrVKO2F9TvAVcCL2oYa3Xgn4D/A+zSte4DLT7WhQ23X9LxPrQ2cEm9PLvgvXIx8AFg65YeyxnAp4AvAhcC/wa8HDgG+FrDWH+kqivuq5f/SDUK/0fgvrb2/zBcpt2c6Yh48TiXHalGS5s4A/gdsFtmbpSZG1ONzv2uXtfEPpk5et73Y4ADMvNZVCPAn2oY6/qIOLRevjYi5gJExLZA01GTRzLz0cy8H/h11l8xZeYDwGMNY62W9afWzFxOVYC9KiI+TVW4NnEy1QeOVwL/AnweeBPwioj4WNO86qke61K9cK1ft68FrNEwFhHxtIg4NiK+EBEbR8SHImJJRJwREZs1DPcYj49+ddqM5vu/c798iqr4/a9Ub0JfbhhrLnAV8H7gD1mNlDyQmT/KzB81jLVaRGwYERtTfROwAiAz/ww80jDWSkXEeQ02P5FqatStVG/eD1CNEv0Y+FLBfa8XER+PiK/VU4k6132xYay5EXFxRHw9IraMiAsi4g8RcWVEvKhhapmZj2Xm+Zl5GFV/+yLVdK6bGsa6h6pfjF4WUU1PWlwv9ywiFkfEByJi64Y5jBfvunEuS4BNG4TaMTPflpmXZeZt9eWyzHwb0HTfn0D12vdN4MCI+GZErFWv27lhLCJir47l9SPiuPoxnhIRTR4jEbFR12Vj4Ir6f3WjhrEWRMQm9fLciLgJuDwibo6IXZvEouqbnwDOBX4KfDkz16eaztjo/4jqdW9XqoGwz9fvRaN6/gYXxtxfnftt74Z5wePTbNeiGjwiM2+h+XvShsAGwMURcUVEHBkRY72n9GrbzHw31TeIzwXemZk/Bo4CXtgw1gnAt4FtMnPdzFyX6oPtupm5XpNAdX9fEBG/iIh7I+K3EbG0btugYax1IuLDEXFD/bq6IiIui4hDmsR5gkFX821fqD71XET15th9eaBhrBtL1o2z/VJgVr18Wde6RqNzVMXgiVRf215OVUDfBPyIappHk1iXA2vXy6t13cfihrEuAnboapsFfBV4tGGsa7uuXzmaI9V87iaxjqz3z83A4VSftv+danTg6II+9n3gnVQv7tcB76EaQXkncHbDWHtRfUNxHtVB4xfW8ZfRMdLcY6zFHcvXdK27pkmsjtttAXyDanSi59G9rhjL6/3/m/rvZnX7OiV5Uf0+YKzLjsAdDeJc3bF8S9e6kry+SfUN1H5UZ279JrBW93PTY6wrqKZfHURV7O9ft+8B/KxhrKtXsm7thrHeXffP53e0/aawX/wG+CRwS/14jwSeXhKrjncX1YDJM7ouc6h+L9FrnMuA1/PE18PVgAOofgvSJKdruq6/H/h/VCP6jfpEdz8CvgJ8pH6MRwLfbhjrsfo56Lw8PPp/2jDWko7li4G/qZe3BRaV9tcx/i+vbhjruo7lWVSvr9+iKmCbxnq043Vs9DJ6/aGGsY6geu/4d6pvvg+t20eASyfQJ15O9YHjzvp5mFfQx67pWD6+a921BfF2pKoNDq//jxr1rY44P6B6r31aR9vT6rbzG8Y6GziE6v3tXcD/BrYBTqL63Vnz/EpuNMwX4HqqT0Fjrbu1YazzqT6NbdrRtmn95P2wYax31vF2p/o6+XNUn5j/hYZfnXTEXI/qk+KONPwavyPGWuO0b0LHG2aPsbbo7Ohd63ZpGOunwMvq5ddQzd8eXdfog0x9m6dTv1FTfYrfH9ipcJ9d3bHcRhG2GtUo1X+rLztTfwXYMM5t9QvDu+sX+ehYN6HpLMA+pS8yK4m5NrBVwe1a+cDc+cYAfKRrXePpJ93PPRMonFbRx65uGGvblp+30Q9Yn6b6tqf0zbHtIuC40deMMdad0iDOHOB04G6qaVi/rJdPb9pfqQZRVutqO4Rq+trNE9xn3f3tmoax2vxg1OZg0c+opiS9nmoAZL+6fVeaF+ZPGngBjq7/L5tOo/sVMHucdY1qi/o2z6V6H9quZJ+P1Sc62lanGqg5oSDeV6ina3a1bw38pDDH1aiK6R/T4INtV4w2BzdbG6z7S4yJPInDeKk757PHWbdfw1gbAv9K9cnxd1TzppfWbY3nB1JNezidag7jEuB7wDzGmDM70y9UHxKuqPf7T0afU6pP7ocPOLdWi7AW8zq66zI6N/lpwFcH/Zy2+Dhb+cBMNSd/rDeNZwFnFuTVWuHUZkHRx+fhNVSjuHcW3r7VIqDlx/YSqiPObAzsAvwzsHdBnE8Ae47RvhcNi7n6dq1+YKa9D0atDRbVr/0/oPq2brs61u/r/6OXNoz1dcb4hg/4R+DhhrHezjjf/FJNhRhUXz2tDzF34vFvF7av+9w+nf2tMNbLgQ8W/i+1ObjZ6mBd5jQspuudsR3V16HrdLU3+tq8I9aeLcZqJa+ZcAGe09a+bzmvVouwlnOb9n2Mdj8wt/la0Vrh1GZB0Yf9/5d9RvVD5eeV7LN+FAEtPb6jqT4kLKL68eCFVF8DXwq8fyL7q6v9VYW5tf6BmQl+MKpj7MbYg0WzCmI9p8X/y7EKw8bFXNux2ry0/DrW3f8vKu3/bf4v8cTBzXt54uDmhg1jvYAnDtZtW7cXD9YNtAP0qVMdDtxINel9ObBvx7qmX7UOZayZcKn31y+m2v6invs2oPt+50zvY032/2Turzb7xYD72KS8jg34MbZ5hKMp08d44gejgfXXNl/76W9hWByr5ee91T7Wcv9vLVabfawfsQby5PfzQruHZxvKWDPhMlX3F4U/0pvJ+2xQ+38y91eb/WIm9LEBP8arx1qur18zjPur7X02yFh9eN+dUoVhwf5ttY+13P9bi9VmH+tHrOl4BsQnHJ4tInYDzoyIZ9D88GzDGmsmGNr9FRHXjbeKZofgatvQ7rM2tbj/W91fbfaLmdDHhvgxPhQRa2d1uNAdRxsjYn0meLjQ6drHWn4u29xnj2Tmo8D9EfGEQ79GRNPnss1YbWr7db/N/t9arCHur8D0PJ34XRGxQ2ZeA5CZf4qIVwPHA8+fJrFmgmHeX5tSHf/6d13tQfXDhkEZ5n3Wprb2f9v7q81+MRP62LA+xr/LzAcBMrPzDX8NqhPdNDFT+libsdrcZ0NZGLas7T7WZv9vM9aw9ldgehbTb6brRBCZ+Qjw5ohoeuKKYY01Ewzz/vou1ddq13SviIhLJj2bxw3zPmtTW/u/7f3VZr+YCX1sKB/j6Jv/GO33UJ20pomZ0sfajNXmPhvWwrBNrfaxNvt/y/9Lw9pfq9vVc0QkSZIkNTTtTicuSZIkTRaLaUmSJKmQxbQkDYGIeFpEnBYRv46IqyLiexGx7aDzGktEPD0izqyXd4iIvQedkyQNisW0JA1YRARwFnBJZm6dmTsC72Wwh4gbV2b+R2buX1/dAbCYljRjWUxL0uC9Ang4M7802pCZ1wI/iYhjIuL6iFgSEQcARMRuEfGjiDg7Im6KiAUR8caIuKLebut6uxMj4tiIuKzebreIOD4ilkbEiaP3FRF/6ljef3RdffvPR8RP69vvX7fPqXNaE/gwcEBEXBMRB0TEryJipN5utYhYNnpdkqYji2lJGrznAVeN0f46qpHfFwJ7AsdExGb1uhcCbwWeA7wJ2DYzdwK+QnWK4VEbAn8LHAmcA3wGeC7w/IjYoYfcNgNeBrwaWNC5IjMfAj4InJ6ZO2Tm6cDXgTfWm+wJXJuZK3q4H0makiymJWl4vQw4NTMfzcy7gB8Bf1OvuzIz76iP5fpr4Py6fQnVaYVHfSerY6AuAe7KzCX1cXJv6NpuPN/OzMcy8+f0Nu3keKpj3wK8BTihh9tI0pRlMS1Jg3cDHWdV61HnCREe67j+GE88IdeDY2zTvV3nCQeespL7WeXpiTPzVqqzsu0O7ASct6rbSNJUZjEtSYN3EbBWRMwbbYiIFwC/p5qPvHo97/jvgCv6cP93RcRzImI14LUNb/tHYN2utq9QTff4RmY+2kaCkjSsLKYlacDqaRivBfasD413A/Bx4BTgOuBaqoL7qMy8sw8pzKc6xe5PgTsa3vZiYPvRHyDWbecA6+AUD0kzgKcTlyS1KiLmAp/JzJcPOhdJ6rdZq95EkqTeRMR84G08fkQPSZrWHJmWJEmSCjlnWpIkSSpkMS1JkiQVspiWJEmSCllMS5IkSYUspiVJkqRC/wnfLX9PYi6QawAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "communities.value_counts().sort_values(ascending=False).plot(kind=\"bar\", figsize=(12, 5))\n",
-    "plt.xlabel(\"Community\")\n",
-    "plt.ylabel(\"# Members\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "communities.loc[\"turkish\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = communities[communities==17].index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['pharmaceutical', 'worth', 'american motors', 'parts', 'auditors',\n",
-       "       'qualified', 'midland', 'salomon', 'consolidated', 'taft', 'goldman',\n",
-       "       'rejects', 'plants', 'wednesday', 'tvx', 'miami', 'jersey', 'broadcast',\n",
-       "       'dudley taft', 'earn', 'audit', 'opinion', 'closing', 'directors',\n",
-       "       'liquidating', 'stations', 'controls', 'radio', 'chrysler',\n",
-       "       'statements', 'gets', 'motors', 'year ending', 'aluminum', 'beverage',\n",
-       "       'near', 'employs', 'renault', 'kentucky', 'bass', 'marine', 'semi',\n",
-       "       'staff', 'share payable', 'brand', 'adding', 'broadcasting', 'car',\n",
-       "       'financing', 'smelter', 'guinness', 'bidder', 'henderson', 'houston',\n",
-       "       'extended', 'david', 'amc', 'mitsui', 'toledo', 'alcan', 'importer',\n",
-       "       'institutional'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nodes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "smallGrap = nx.subgraph(filteredEntityGraph, nbunch=nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "\n",
-    "pos = nx.spring_layout(smallGrap) # k regulates the distance between nodes\n",
-    "\n",
-    "nx.draw(smallGrap, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos)\n",
-    "\n",
-    "# plt.show()\n",
-    "# plt.savefig(os.path.join(\".\", \"CloseUp.png\"), dpi=300, format=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we show a Bipartite Closeup of the cluster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bipartiteCloseup = subGraph.edge_subgraph(\n",
-    "    {e for e in subGraph.edges() if len(set(e).intersection(nodes))>0}\n",
-    ")\n",
-    "\n",
-    "deg = nx.degree(bipartiteCloseup)\n",
-    "\n",
-    "smallGrap = nx.subgraph(bipartiteCloseup, {n for n, d in bipartiteCloseup.nodes(data=True) if d[\"bipartite\"]==1 or deg[n]>1})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "480"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len([n for n, d in bipartiteCloseup.nodes(data=True) if d[\"bipartite\"]==0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "62"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "\n",
-    "pos = nx.kamada_kawai_layout(smallGrap) # k regulates the distance between nodes\n",
-    "\n",
-    "node_color = [\"skyblue\" if d[\"bipartite\"]==1 else \"red\" for n, d in smallGrap.nodes(data=True)]\n",
-    "\n",
-    "nx.draw(smallGrap, with_labels=False, node_color=node_color, #'skyblue', \n",
-    "        node_size=150, edge_cmap=plt.cm.Blues, pos = pos)\n",
-    "\n",
-    "\n",
-    "# plt.show()\n",
-    "# plt.savefig(os.path.join(\".\", \"BipartiteCloseUp.png\"), dpi=300, format=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using TSNE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing transition probabilities: 100%|██████████| 2265/2265 [00:07<00:00, 321.05it/s]\n",
-      "Generating walks (CPU: 1): 100%|██████████| 10/10 [01:45<00:00, 10.55s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from node2vec import Node2Vec\n",
-    "\n",
-    "node2vec = Node2Vec(filteredEntityGraph, dimensions=5) \n",
-    "model = node2vec.fit(window=10) \n",
-    "embeddings = model.wv "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.manifold import TSNE\n",
-    "tsne=TSNE(n_components=2)\n",
-    "embedding2d=tsne.fit_transform(embeddings.vectors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a672ab90>]"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(embedding2d[:, 0], embedding2d[:, 1], 'o')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using Node2Vec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Node2Vec allows also to compute a similarity between entities"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('turkish', 0.9921346306800842),\n",
-       " ('lira', 0.987409234046936),\n",
-       " ('debts', 0.9794315099716187),\n",
-       " ('coastal', 0.9783217906951904),\n",
-       " ('athens', 0.9770432710647583),\n",
-       " ('greece', 0.9727554321289062),\n",
-       " ('benefits', 0.9630903601646423),\n",
-       " ('carolina', 0.962989330291748),\n",
-       " ('sharp', 0.9628170728683472),\n",
-       " ('jones', 0.9522427320480347)]"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embeddings.most_similar(positive=[\"turkey\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Document-Document Graph Projection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from networkx.algorithms.bipartite.projection import overlap_weighted_projected_graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documentGraph = overlap_weighted_projected_graph(\n",
-    "    G, \n",
-    "    {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 0}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 10788\n",
-      "Number of edges: 12994465\n",
-      "Average degree: 2409.0591\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(documentGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = pd.Series({k: v for k, v in nx.degree(documentGraph)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(degrees, 100)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in documentGraph.edges(data=True)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.01, 1)"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(allEdgesWeights, 100)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlim([1E-2, 1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Edge Weight Distribution')"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plotDistribution(degrees, 13)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Degree Distribution\")\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plotDistribution(allEdgesWeights, 20)\n",
-    "plt.xlim([1E-2, 10])\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Edge Weight Distribution\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filteredDocumentGraph = documentGraph.edge_subgraph(\n",
-    "    allEdgesWeights[(allEdgesWeights>0.6)].index.tolist()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 1958\n",
-      "Number of edges: 7884\n",
-      "Average degree:   8.0531\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(filteredDocumentGraph))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Global and Local Properties"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = pd.Series({k: v for k, v in nx.degree(filteredDocumentGraph)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD/CAYAAAAKVJb/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOTklEQVR4nO3dT4hd53nH8e8TGZcg0VnUYRaS6SjImAp7UTzYdDeCph2Tyg6uIVJcUwfZgwPuKosqEGg3pcnCi9pxa9TYqCnGU2NKbFkK7mowBS8khcJYMW6FcbFmYZGoDMi4GIWni7m1b0ZzZ86dc/8+/n5gYO573/Oe50ovP86859xzIjORJNXypXEXIEkaPMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoaeLhHxO9FxPMR8WpEfGfQ40uSdhZNvqEaES8CfwJczcy7utoXgb8D9gA/zswfdL33JeAnmflnO41/22235dzcXP/VAx9//DF79+7d1bbSTpxfGrY2c+zixYu/zMyvbPXeLQ3HOA38CPjJ/zdExB7gOeBrwBXgfES8npm/iIgHgO8A/9xk8Lm5OS5cuNCwlN+0srLCwsLCrraVduL80rC1mWMR8d+93mu0LJOZbwHXNjXfC1zOzPcz81NgGXiw0//1zLwfeGRXFUuSWml65L6V/cCHXa+vAPdFxALwEPBbwLleG0fEErAEMDs7y8rKyq6KuH79+q63lXbi/NKwDWuOtQn3LWXmCrDSoN8p4BTA/Px87vbPEv9s1jA5vzRsw5pjba6WWQNu73p9oNMmSRqzNuF+HrgjIg5GxK3AMeD1fgaIiKMRcWp9fb1FGZKkzRqFe0S8DLwN3BkRVyLiRGbeAJ4C3gTeBV7JzEv97Dwzz2Tm0szMTL91S5K20WjNPTOP92g/xzYnTSVJ4zHW2w8MYllmdW2duZNnmTt5doCVSdJ0G2u4uywjScPhjcMkqSDDXZIKmvo1d0nSzVxzl6SCXJaRpIIMd0kqyHCXpII8oSpJBXlCVZIKcllGkgoy3CWpIMNdkgryhKokFeQJVUkqyGUZSSrIcJekggx3SSrIcJekggx3SSrISyElqSAvhZSkglyWkaSCDHdJKshwl6SCDHdJKshwl6SCDHdJKsjr3CWpIK9zl6SCXJaRpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIJuGXcBXyRzJ89+9vsHP/j6GCuRVJ33lpGkgry3jCQV5Jq7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQd7ytzBvMSx9cXnkLkkFGe6SVNBQlmUi4hvA14HfBl7IzH8bxn4kSVtrfOQeES9GxNWIeGdT+2JEvBcRlyPiJEBm/jQznwCeBL452JIlSTvpZ1nmNLDY3RARe4DngPuBw8DxiDjc1eX7nfclSSPUONwz8y3g2qbme4HLmfl+Zn4KLAMPxoYfAj/LzJ8PrlxJUhORmc07R8wBb2TmXZ3XDwOLmfl45/WjwH3AfwJ/DpwH/iMzn99irCVgCWB2dvae5eXlXX2Aq9fW+eiTjd/v3j/Zz2JdXfv8QeCjqHXU+6vo+vXr7Nu3b9xlqLA2c+zIkSMXM3N+q/eGckI1M58BntmhzyngFMD8/HwuLCzsal/PvvQaT69ufIwPHtndGKPyWPd15yOoddT7q2hlZYXdzk2piWHNsbaXQq4Bt3e9PtBpkySNUdsj9/PAHRFxkI1QPwZ8q+nGEXEUOHro0KGWZWiYJvGbrpNYkzRJ+rkU8mXgbeDOiLgSEScy8wbwFPAm8C7wSmZeajpmZp7JzKWZGdeDJWmQGh+5Z+bxHu3ngHMDq0iS1NpYbxzmsswXg0so0uiN9d4yLstI0nB44zBJKshwl6SCxhruEXE0Ik6tr6/v3FmS1Jhr7pJUkI/Z08Tx6hqpPdfcJakgw12SCvKEqiQV5AlVSSrIZRlJKshwl6SCDHdJKsgTqpJUkCdUJakgl2UkqSDDXZIKMtwlqSBvHKbSvAmZvqg8cpekgrwUUpIKGuuyTGaeAc7Mz88/Mc46pH641KNp4Jq7vvAMa1XkmrskFWS4S1JBhrskFeSau6QtdZ+L2MxzE5PPI3dJKmisR+4RcRQ4eujQoXGWIU0cr+BRW97yV+ph7uRZVtfWt12ekCaVyzKSVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkF+Q1VaUC8Nl2TxCN3SSrIcJekggx3SSrIZ6hKUkHeW0aSCvJqGUlD4dVD4+WauyQV5JG7NGSTcgQ7KXVoNDxyl6SCDHdJKshwl6SCDHdJKshwl6SCDHdJKshwl6SCDHdJKshwl6SCDHdJKshwl6SCvLeMNELb3d+l+z2prYEfuUfEVyPihYh4ddBjS5KaaRTuEfFiRFyNiHc2tS9GxHsRcTkiTgJk5vuZeWIYxUrSKM2dPPvZz7RpeuR+GljsboiIPcBzwP3AYeB4RBweaHWSpF1pFO6Z+RZwbVPzvcDlzpH6p8Ay8OCA65Mk7UJkZrOOEXPAG5l5V+f1w8BiZj7eef0ocB/wV8DfAF8DfpyZf9tjvCVgCWB2dvae5eXlXX2Aq9fW+eiTjd/v3j/Zz2JdXfv8QeCjqHVQ+2s7Tr/bN+nfdMw2Y62urTP7Zfjok5vbe/Xfqb3b5np69dtum630W1+TcbarY1D7m0Sj+AzXr19n3759u9r2yJEjFzNzfqv3Bn61TGb+CniyQb9TwCmA+fn5XFhY2NX+nn3pNZ5e3fgYHzyyuzFG5bHuKyVGUOug9td2nH63b9K/6Zhtxnrs5Fm+e/cNnl695ab2Xv13au+2uZ5e/bbbZiv91tdknO3qGNT+JtEoPsPKygq7zb/ttLlaZg24vev1gU6bJGnM2hy5nwfuiIiDbIT6MeBb/QwQEUeBo4cOHWpRhqQ2fLZqTU0vhXwZeBu4MyKuRMSJzLwBPAW8CbwLvJKZl/rZeWaeycylmZnpXI+TpEnV6Mg9M4/3aD8HnBtoRZKk1ry3jCQVNNZ7y7jmLg3GNH6DcppNw3mKsR65u+YuScPhsowkFeSyjKTPTOvyzua6+10qmYZlln65LCNJBbksI0kFGe6SVJDhLkkFeUJVUt96nXhtckK218nLiic1x8kTqpJUkMsyklSQ4S5JBRnuklSQJ1SlKTWt3ybt1u9naHrSdVwnZyfppLAnVCWpIJdlJKkgw12SCjLcJakgw12SCjLcJakgL4WUCmtzD5gqJunyxFHyUkhJKshlGUkqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyC8xSZpK0/xFrO7aTy/uHco+/BKTJBXksowkFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JB3ltG0tD1ex+YUTzUuk1N08B7y0hSQS7LSFJBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBA39YR0TsBf4e+BRYycyXBr0PSdL2Gh25R8SLEXE1It7Z1L4YEe9FxOWIONlpfgh4NTOfAB4YcL2SpAaaLsucBha7GyJiD/AccD9wGDgeEYeBA8CHnW6/HkyZkqR+RGY26xgxB7yRmXd1Xv8B8NeZ+ced19/rdL0C/E9mvhERy5l5rMd4S8ASwOzs7D3Ly8u7+gBXr63z0Scbv9+9f7If17e6tv7Z76OodVD7aztOv9s36d90zDZjra6tM/tl+OiTm9t79d+pvdvmenr167VNk/76TcP492v7f31wZg/79u3b1b6PHDlyMTPnt3qvTbg/DCxm5uOd148C9wF/CfwI+F/g35usuc/Pz+eFCxca1bHZsy+9xtOrG6cOhvUg3UEZxUN/h7G/tuP0u32T/k3HbDPW3MmzfPfuGzy9estN7b3679TebXM9TR7A3GRc9TaMf7+2/9enF/eysLCwq31HRM9wH/gJ1cz8GPj2oMeVJDXX5lLINeD2rtcHOm2NRcTRiDi1vu6fl5I0SG3C/TxwR0QcjIhbgWPA6/0MkJlnMnNpZmay18olado0vRTyZeBt4M6IuBIRJzLzBvAU8CbwLvBKZl4aXqmSpKYarbln5vEe7eeAcwOtSJLU2lhvP+CauyQNx1jD3TV3SRoObxwmSQU1/hLTUIuIWAf+a5suM0CvtZvbgF8OvKjh2+4zTfK+2ozV77ZN+zfpt10f59fk7Kvi/Nrp/TZz7Hcz8ytbvpOZY/8BTu32feDCuOsfxmee1H21GavfbZv2b9Jvhznk/JqQfVWcXzu9P6w5NinLMmdavj+NRvmZBrmvNmP1u23T/k36bdfH+TU5+6o4v/rZ18BMxLJMGxFxIXvcW0Fqy/mlYRvWHJuUI/c2To27AJXm/NKwDWWOTf2RuyTpZhWO3CVJmxjuklSQ4S5JBZUL94jYGxH/FBH/GBGPjLse1RIRX42IFyLi1XHXonoi4hud7PqXiPijNmNNRbhHxIsRcTUi3tnUvhgR70XE5Yg42Wl+CHg1M58AHhh5sZo6/cyvzHw/M0+Mp1JNoz7n10872fUk8M02+52KcAdOA4vdDRGxB3gOuB84DByPiMNsPBHqw063X4+wRk2v0zSfX1K/TtP//Pp+5/1dm4pwz8y3gGubmu8FLneOpD4FloEHgStsBDxMyefTePU5v6S+9DO/YsMPgZ9l5s/b7Heaw28/nx+hw0ao7wf+FfjTiPgHan6tXKOx5fyKiN+JiOeB34+I742nNBXQK7/+AvhD4OGIeLLNDho9iWmaZObHwLfHXYdqysxfsbEeKg1cZj4DPDOIsab5yH0NuL3r9YFOmzQIzi8N09Dn1zSH+3ngjog4GBG3AseA18dck+pwfmmYhj6/piLcI+Jl4G3gzoi4EhEnMvMG8BTwJvAu8EpmXhpnnZpOzi8N07jmlzcOk6SCpuLIXZLUH8Ndkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpoP8DIetzTktsTukAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(degrees, 100)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in filteredDocumentGraph.edges(data=True)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.1, 1)"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(allEdgesWeights, 100)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlim([1E-1, 1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Edge Weight Distribution')"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "\n",
-    "plt.subplot(1,2,1)\n",
-    "plotDistribution(degrees, 13)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Degree Distribution\")\n",
-    "\n",
-    "plt.subplot(1,2,2)\n",
-    "plotDistribution(allEdgesWeights, 20)\n",
-    "plt.xlim([1E-2, 10])\n",
-    "plt.yscale(\"log\")\n",
-    "plt.title(\"Edge Weight Distribution\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Core - Periphery Description and Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Create network layout for visualizations\n",
-    "spring_pos = nx.spring_layout(filteredDocumentGraph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.axis(\"off\")\n",
-    "nx.draw_networkx(filteredDocumentGraph, pos=spring_pos, node_color=default_node_color, \n",
-    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "components = pd.Series({ith: component \n",
-    "              for ith, component in enumerate(nx.connected_components(filteredDocumentGraph))})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(components.apply(len), nbins=20)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coreDocumentGraph = nx.subgraph(\n",
-    "    filteredDocumentGraph,\n",
-    "    [node for nodes in components[components.apply(len)>8].values for node in nodes]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# To be visualized in Gephi\n",
-    "nx.write_gexf(coreDocumentGraph,\"coreGraph.gexf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 1050\n",
-      "Number of edges: 7112\n",
-      "Average degree:  13.5467\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(coreDocumentGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "degrees = pd.Series({k: v for k, v in nx.degree(coreDocumentGraph)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6694: RuntimeWarning: invalid value encountered in multiply\n",
-      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(degrees, 100)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in coreDocumentGraph.edges(data=True)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.1, 1)"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotDistribution(allEdgesWeights, 100)\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlim([1E-1, 1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Create network layout for visualizations\n",
-    "spring_pos = nx.spring_layout(coreDocumentGraph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.axis(\"off\")\n",
-    "nx.draw_networkx(coreDocumentGraph, pos=spring_pos, node_color=default_node_color, \n",
-    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Community Detection and Topics Clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities = pd.Series(community.best_partition(coreDocumentGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities = pd.Series(community.best_partition(filteredDocumentGraph))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "def getTopicRatio(df):\n",
-    "    return Counter([label for labels in df[\"label\"] for label in labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communityTopics = pd.DataFrame.from_dict({\n",
-    "    cid: getTopicRatio(corpus.loc[comm.index])\n",
-    "    for cid, comm in communities.groupby(communities)\n",
-    "}, orient=\"index\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "normalizedCommunityTopics = (communityTopics.T / communityTopics.sum(axis=1)).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "normalizedCommunityTopics.std(axis=1).fillna(0).hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Entropy')"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "normalizedCommunityTopics.apply(lambda x: np.mean(-np.log(x)), axis=1).hist()\n",
-    "plt.xlabel(\"Entropy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topicsCorrelation = normalizedCommunityTopics.corr().fillna(0)\n",
-    "topicsCorrelation[topicsCorrelation<0.8]=0\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topicsGraph = nx.from_pandas_adjacency(topicsCorrelation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8,8))\n",
-    "\n",
-    "pos = nx.spring_layout(topicsGraph, k=0.35) # k regulates the distance between nodes\n",
-    "\n",
-    "nx.draw(topicsGraph, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos)\n",
-    "\n",
-    "# plt.show()\n",
-    "# plt.savefig(os.path.join(\".\", \"TopicsAll.png\"), dpi=300, format=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filteredTopicsGraph = nx.subgraph(\n",
-    "    topicsGraph,\n",
-    "    [node for component in nx.connected_components(topicsGraph) if len(component)>3 for node in component]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oxH4oZDIoZDK8enQfsdERBRqXAQOJkBInQkoSSpxy4OjoiCVLlsDT0xNGRka4fv066jVogGnfVcaqXs2yNc0LPLYXSzvUwaxGjljavjbu+//3zvTekd1Y0bUh5jR1xrbRPRD/5lXW56bWKo9b+7ZieSdv/N65Ho4snAz/lbOyxbFjwve4tmsDACDpwzvsmjQI833csbR9bdzYuznrcdEhgahVuw5MTExgZWWFiRMnauqfRieoq75JhQrE+a28gRDyYm6HNeo/CtVbd8S20T0xu3ElHJz7E+QZ6dAzMsYP6w8g5OxhLGpTHQtaV8PpVXMhlxWsKaqcZWFpQAv7hJQkDEsXn33D0dERZmZmOH78OAQCATw9PbF00zYkODXE49tXsHfqcEw8dBMSfQMsbO2BMTvPobyjM5I+vENaUgKsnNzx+PIpnFzxGwb+sRsW9pVxZfsqhF+/gFHb/QFkJk7O9Zqiz+ItEOvp4/Wj+9g3YxSm+AeDYRikJSVgYZvqmHzsLowtLLGufytUbeaLpoN/RNL7N/hzVHd0nroUrg19sHGQL8aNGY2fhw9GSkoKQkNDUb9+fY7/Fbnj6OiIs2fPwtXVVS3jsSwLCwsLPHr0CNbW1moZk6jXypA4ZPCwCFtPyGCCpwXXYRBC1IhWnHLx448/ws7ODrt27UK7du3wXYu2YAQMXOo3g20VL4RfPw8AYBgB3j8Ngyw9DSblK8DKyR0AEPDvdjT7YTwsK7tCKBKh2Q8T8DYiNNuqU7PB42Foag6xvgEcazUAGAbPg24BAB6ePw776nVgUr4CXj+6j9SEOLQYPgkisQRlbR3h3aU/Qs4cBgAIRSJER0Xh48ePMDY2LtVJU1xcHD59+gRnZ2e1jckwDOrWrUvbdTxmqS/kOoQc8TUuQkjR0RpyLuzs7AAAL168wIEDB3D02HHI/t9kTyGXo3LdRpAYGKHP4i24tnM9Ds75CQ41vNFuwlxYVnJBwtvXOLFsOvxX/Lf9xrIskj68hblN5timFWyyPscwDLxad8GDM4dRqXZDPDh9EDXadQcAJLx9heQP77Kf7FEq4FgzM0HqMesPhPz9O9zd3VGpUiXMmjUL7du31+w/EE8FBQWhZs2aEAjU+55AVefUsWNHtY5L1MPZVIK3n+W8OrwhYjLjIoSULJQ45UJ1+sbOzg7ff/89fl66Fv4vkyH9qmWSa0MfuDb0gSw9DWfXL8LheRMwYtsJmFrZoNmQCaj5/+Qnl1my/cmrbRdsG90TTQf9iFehQej/+98AAFOrijC3scekoznX2Vg6OGHFnzvhairGoUOH0L17d8TFxcHIyKjIf39dFRQUpNb6JpW6deti3bp1ah+XqIenhT6uvv3MdRjZsMiMixBSstBWXT769++P48eP4/alc2AVSsgy0hF97wYS379BclwsHl8+BWlaKoQSPUgMjMD8f6WjXvdBuPLXKrx/+gQAkJ6chIfnjuY5l427JwzNyuLQvAlwadAcBmVMAQB2HrWgZ2SMK9tXQ5aeBqVCgXdRYXj16D4AIPDkASTHf4RAIICZmRkAqH3FRVcEBgaq9USdimqrjkoC+clAJICLqYQ3fdYYAC6mEhiISuf3ISElGa045cPOzg5Hjx7Fz5MnI+jBQwgEQth61ETnqcvAKpW4vmsD9v82BgwYWLt5oNO0ZQCAaj5+yPicir1ThyPh7SvoG5vAuV5TVG/VKc/5vHy74fyGxei75M+sjwmEQgxctQcnV/yGpR1qQy6VoryDM1qPmQoACLtxEe3+mIW0z5/h4OCAf/75BwYGBrlNUaIFBQVhzpw5ah/X2toaxsbGePr0qVrrp4j6eFsa8KbXmpAB6lmWzu9BQko6OlVXCHRyh98SEhJgZ2eHhIQECIXqL8rt1q0bunXrhr59+6p9bKIeh6OTEMXxJdzC/9c2dalkwl0QhBCNoXXkQuDrCRm+xqVt9+/fh5eXl0aSJoAaYeqCtvbGEAm43bATCRj42hlzGgMhRHMocSoEZ1MJRHwpovg/OrnzH03VN6lQI0z+MxAJ0N7BGGKOXtnEAqC9gzH0qbaJkBKLvrsLwdNCv4B3sGsPndz5j6ZO1KnUrl0bDx48gEwm09gcpPhcTPVQw0Jf68mTWADUsNCHi6medicmhGgVJU6FQCd3+E3dV618zdTUFHZ2dnj06JHG5iDq4VPRCFXM9LSWPIkFQFUzPfhULH0tQAgpbegnbiF5WxqAL3d20smd/yQlJSEmJgbu7u4anYfqnHQDwzDwtTfWysqTaqWprb1xVv83QkjJRYlTIdkYieFkIuE8eRIygJOpBNZGYm4D4Yng4GBUr14dIpFmO2xQnZPuYBgGLWyN0dGxDPSEjNq/Z4VM5onWjo5l0MKWkiZCSgtKnIqATu7wT1BQkEYLw1VoxUn3uJjqYWRVczibZB7uKO53LoP/DmWMrGpONU2ElDKUOBUB1yd3oJChvT2d3PmSpuubVLy8vBAREYHU1FSNz0XUx0AkQJfKJujnYgo3s8wV48KekBUxmatMbmYS9HMxRZdKJlRfSEgpRN/1RcTVyR0hWISdPYwVU3+CXC7X7uQ8pq0VJz09PVSrVg3379/X+FxE/ayNxOhcyQRjPcrCy0iBlw8Csrbx9AQMJILMmiWJIPPPqu04OyMRGlsbYqxHWXSuZEJb5ISUYtQ5vBhYlsWplykIS8iATJn/44tLdXKnoZkSvXr1gkAgwL59+1CmTBnNT85jqampsLS0REJCAsRizf9AGzNmDJydnTFhwgSNz0U058iRI9i8eTP8/f2RIlMiNk0OqYKFgmUhZBhIhAwsDUQw5mxpmRDCR/SKUAxcndwxNTXF8ePHYWdnh8aNG+P169eanZznHjx4gKpVq2olaQKoQLykuH37NurVqwcAMBYLUNlEAndzPVQrqw93cz1UNpFQ0kQI+Qa9KhQTVyd3xGIxNm7ciL59+6JBgwYIDg5W78Q6RFv1TSpUIF4yBAQEoH79+lyHQQjRMZQ4qQkXJ3cYhsEvv/yCFStWoHXr1vD39y/mrLpJW/VNKm5uboiNjUVcXJzW5iTqpVAoEBgYCG9vb65DIYToGEqc1Iirkzs9evTA0aNHMWTIEGzYsKEYfwPdpO0VJ6FQiNq1a+PevXtam5Oo16NHj2BjYwNzc3OuQyGE6BjNdgsspTJP7oiRJlciJC4dUYlSxKYrIFeyEDEMWLBgkbmqxICBnGUhEjCw1BfC2VQCTwv9Qh9zbtCgAa5fvw4/Pz88ffoUS5cuhUBQ8vPitLQ0REVFwcPDQ6vzquqc2rRpo9V5iXoEBARk1TcRQkhhUOKkQQYiAepZGaKelSEAaPzkjpOTE27evIkuXbqgR48e2LlzJwwNDYs9Lp+FhITA3d0denrabULo7e2NHTt2aHVOoj5fFoYTQkhhlPwlCR7RxsmdsmXL4uzZszA0NETz5s3x/v17tY3NR4GBgVqtb1JRrThRNw/dRIXhhJCiosSpBNLT08OOHTvg6+uLBg0aICwsjOuQNCYoKEir9U0q9vb2YFkWr1690vrcpHiSkpLw/PlzVK9enetQCCE6iBKnEophGMyePRuzZs1Cs2bNcOnSJa5D0giuVpwYhqG2BDrq7t27qFmzptb6fhFCShZKnEq4gQMH4p9//kHv3r3x999/cx2OWmVkZCA8PByenp6czE+NMHUTFYYTQoqDEqdSoHnz5rh8+TLmzJmDWbNmlZi6nIcPH8LZ2RkGBgaczE8rTrqJCsMJIcVBiVMpUaVKFdy6dQunT5/G999/j4yMDK5DKjau6ptU6tSpg3v37kGp1MJFhUQtWJalwnBCSLFQ4lSKWFlZ4dKlS0hPT0fr1q3x6dMnrkMqFq7qm1TKlSuH8uXLIzw8nLMYSOE8f/4cQqEQtra2XIdCCNFRlDiVMoaGhti/fz+8vb3RoEEDPH36lOuQiozrFSeA6px0jWq1SXXnIyGEFBYlTqWQQCDAsmXL8NNPP6FRo0a4desW1yEVmlQqxaNHj+Dl5cVpHFTnpFuoMJwQUlyUOJVio0aNwrZt29CpUyccOHCA63AK5fHjx6hUqRKMjIw4jYNWnHTL7du3qb6JEFIslDiVcr6+vjh37hwmTpyIJUuW6MyJO21f7JubWrVqITQ0tEQU25d0GRkZCAkJ4cXXDSFEd1HiRODl5YVbt25h7969GDFiBGQyGdch5SsoKIjTwnAVIyMjuLi4ICQkhOtQSD4ePHgAZ2dnGBsbcx1KiZMsUyA6SYon8Rl49CkdT+IzEJ0kRYqMTpySkocu+SUAAFtbW1y7dg29evVC+/btceDAAZiYmHAdVq4CAwPRu3dvrsMA8N92Xd26dbkOheSB2hCoT5pciZC4dEQlShGbroBcyULEMGDx34o1AwZyloVIwMBSXwhnUwk8LfRhIKL360S30VcwyVKmTBkcO3YMTk5OaNSoEW/vYZPL5Xj48CFq1KjBdSgAqEBcV1BhePG9SZXhyLMkrA39hGtvP+NVqhwZChYKFshQspAqkfUrQ/n/jytYvEqV49rbz1gb+glHniXhTSr/V7UJyQ0lTiQbkUiEdevWYdCgQWjQoAGCgoK4DukbYWFhsLOzQ5kyZbgOBQAViOsKKgwvujS5Eoejk7AnMhHhCVIoWEBeyHJIOQsoWCA8QYo9kYk4HJ2ENDlt5RHdQ4kT+QbDMJg4cSJWr16NNm3a4MSJE1yHlA1f6ptUPDw88OLFCyQlJXEdCsnFx48f8fHjR7i7u3Mdis6JTMzAxsfxiEqSQs4CxT0+wiIziYpKkmLj43hEJtLBCqJbKHEiueratStOnDiB4cOHY+3atVyHk4UvJ+pUxGIxvLy8EBgYyHUoJBcBAQGoU6cOBAJ6ySsolmVx4XUKjj1PztqOUyfVNt6x58m48DpFZ070EkKvIiRP9erVw40bN7Bu3TpMmDABCoWC65A4v2olJ1TnxG9UGF44LMvi1MsUBMelQ9MH42RKIDguHadeUvJEdAMlTiRflSpVws2bN/HgwQN069YNqampnMWiUCjw4MED1KxZk7MYckJ1Tvx2+/ZtKgwvhIsxqQhLyNB40qQiUwJhCRm4GMPdawshBUWJEykQc3NznD59GmZmZmjWrBnevXvHSRzh4eGoUKECzMzMOJk/N97e3pQ48ZRSqcTdu3cpcSqgyMQMraw0fU218kQ1T4TvKHEiBSaRSPDXX3+hY8eOqF+/Ph49eqT1GPhwsW9OnJ2dkZKSwllCSXIXEREBc3NzWFpach0K76XJlTjxIkXrSZOKTAmceJFCp+0Ir1HiRAqFYRjMnDkT8+fPR/PmzXH+/Hmtzs/H+iYg89+lTp06VOfEQ7RNV3CnX6ZAruS2zkiuZHH6VQqnMRCSF0qcSJH0798fBw4cQL9+/bBt2zatzcvXFSeACsT5igrDC+ZNqgxPk6RqPz1XWAoWeJooxVtqkkl4ihInUmRNmzbF1atXsWDBAsyYMUPjJ2KUSiXu37/Pu8JwFSoQ5ydacSqYO7FpnCdNKgoWCIhN4zoMQnJEiRMpFjc3N9y+fRsXL15Ev379kJ6errG5oqKiYGFhAQsLC43NURyqFSc6Us0fnz9/RkREBG+Tbb5IkysRmSgtdnNLdWEBRCZKqdaJ8BIlTqTYypcvjwsXLkAul6NVq1b4+PGjRubha32TirW1NQwMDBAdHc11KOT/AgMD4eHhAT09Pa5D4bWQuHQwXAfxFQaZcRHCN5Q4EbUwMDDAP//8g++++w4NGzZEZGSk2ufgc32TCtU58QvdT1cwUYnSQt89p2lyNjMuQviGEieiNgKBAIsXL8bkyZPRuHFjXL9+Xa3j833FCaA6J74JCAig+qYCiE3n/kYAFZZloVRmbtHxKS5CVERcB0BKnmHDhsHe3h5du3bF6tWr0bt372KPybKszqw4zZo1i+swyP/dvn0bS5Ys4ToMXkuWKb5pQXBl+2rc3LsF6anJMClfAZ1+XYr7/vthammD1mOmAQCi793AvhmjMPV0CAAgJuwBDs79CXGvnsG1oQ8YRoBy9pXResw0pCUlYN+M0XgdGgSFQg4HL290mb4cplY2AIDNwzrBwcsbzwJvIObJQ4zfdwXl7CtDrmSRIlPCWEzv8Ql/0Fcj0Yg2bdrg/PnzmDJlChYuXFjsguno6GiYmJigfPnyaopQM+rUqYPg4GDI5XKuQyn1YmJikJGRgcqVK3MdCq99SFNAxPxX4fTheRRu7duKMbvOYc715/hh3X6Y29jlOYZcJsWunwehdofe+O1SJLzadMXjS/5Zn1cqlajTqQ9+ORmEX/2DIdbXx7Elv2Yb477/AXSZsQKzrz2DuXXmfCKGQWwafS8RfqHEiWiMp6cnbt26hX///RfDhg2DTFb0vixBQUG836YDAFNTU9ja2nLSVZ1kp9qmYxi+lT3zi1TBgv3iPB0jEEAulSI2OhwKmQzmNvawsKuU5xivQu5BqZCjYZ/hEIrF8GjRHrYe/51kNDIrC48WHSAxMISekTGaD5mA6MCb2cao3aE3rJzcIRSJIBSLAQAsWEj50iOBkP+jxIlolI2NDa5evYr379+jXbt2SExMLNI4gYGBvN+mU6ECcX6gwvCCUXy1GlzOvjLaT5qP85uWYn7LKtj76zAkfcj7KqGkj+9hYmmdLUk1taqY9Xtp2mccnv8zlrSridmNK2Hz0I5IT06EUqHI8fEqbA7xEcI1SpyIxhkbG+PIkSNwd3fHd999hxcvXhR6DF1ZcQKoQJwvqDC8YIQ5rMjV8O2GkdtOYsrJ+wDD4NSquZDoG0Ka/l9TyuS42KzflylniaTYt9m25BPfx2T9/tquDfjwIgqjd5zG7GvPMPzPYwCQ7fE5LQwyucRHCJcocSJaIRQKsWbNGgwbNgwNGzbEvXv3CvxclmVpxYkUilwuR2BgIOrWrct1KLwnETJgkL3G6emda5BLMyDS04NYTx+MgIG1W3VE3DiPz4nxSP74Hjd2b8p6jr1nXTBCIW7t+xMKuRyPL5/C69D7WZ+XpqZArKcP/TKm+JwYjwublxcoNgYMJEJKnAi/UOJEtGr8+PFYt24dfH19cfTo0QI95+XLl9DT00OFChU0HJ16eHl5ITw8HJ8/f+Y6lFIrNDQU9vb2MDMz4zoU3itvIIT8i5UfuTQDp9fMwzwfNyxsVQ0pnz6i7biZqOnXAxVcqmFp+1rYNronPFt3znqOSCxB/+Xbce/Ibsxt6oT7/gfg3rg1hJLMxqPf9RsBWUY65vu4Yf3AtnBt4FOg2OQsC0sDOvxN+IVh6X4IwoG7d++ic+fO+OWXXzB+/Pg8H3vo0CH89ddfOH78uJaiK746depg9erVaNiwIdehlEobN25EQEAA/vrrL65D0QkrQ+KQoeYi7HUD2qBet4Go06lvkcfQEzKY4MnPK5ZI6UUrToQTdevWxY0bN7B582b8+OOPUChyb3SnS/VNKlTnxK2AgAAqDC8ES31hsceIDryB5I/voZDLEXj8H7yLfAzXhgVbWdJkXISoGyVOhDOOjo64ceMGwsLC0KVLF6SkpOT4OF2qb1Lx9vamxIlDVBheOM6mEoiKWUr08flTrO7dHHObOuH6zg3ot3QrTMoXfXtdxGTGRQjf0FYd4ZxMJsPIkSMRHByMEydOwNraOutzLMvCysoKQUFBsLW15TDKwnn06BE6d+6skTv7SN4SEhJgZ2eH+Ph4iERUH1MQaXIl1oZ+Ap9aJgkZYKxHWRiI6P094Rf6iiScE4vF+PPPP9GtWzfUr18fDx8+zPpcTEwMGIZBxYrf9njhM3d3d7x79w6fPn3iOpRS5+7du6hVqxYlTYVgIBLAxVQCvpxfYwC4mEooaSK8RF+VhBcYhsG0adOwePFitGjRAmfPngXw38W+utb9WSgUonbt2oVqu0DU4/bt27RNVwTelgbgy8l/IQPUszTgOgxCckSJE+GVPn364NChQxgwYAC2bNmiExf75oYKxLlBheFFY2MkhpOJhPPkScgATqYSWBuJuQ2EkFxQ4kR4p1GjRrh27RqWLl2K3bt3o0aNGlyHVCTUCFP7WJalwvBiaGtvDJGA28xJJGDga2fMaQyE5IUSJ8JLLi4uuHXrFl6+fInt27cjPT2d65AKTbXiROcvtCc6Ohr6+vo6VxPHFwYiAdo7GEPM0U8GsQBo72AMfaptIjxGX52Et2QyGcqUKQNjY2O0aNECHz584DqkQnFwcIBCoUBMTEz+DyZqQatNxediqocaFvpaT57EAqCGhT5cTPW0OzEhhUSJE+EtVX3Tnj170KxZMzRo0ADh4eFch1VgDMNQnZOWUWG4evhUNIKVMhXSNO1cGyQWAFXN9OBT0Ugr8xFSHJQ4Ed5SnagTCARYsGABpk6diiZNmuDq1atch1Zg1AhTu6gwXD1iYmIwrVNjlEv7oPGVJ9VKU1t7Y507PUtKJ0qcCG99faJuyJAh2L17N7p3747du3dzGFnBUYG49qSnpyM0NFRnT2HyRVJSEvz8/DB27FiMblEbHR3LQE/IqP20nZDJvIuuo2MZtLClpInoDuocTnjLzs4Oly9fhpOTU7aPh4aGon379hg6dCimT5/O6xfcjx8/wsnJCfHx8RAI6H2KJt2+fRujR49GUFAQ16HoLJlMhg4dOsDR0REbNmzI+t5Kkytx+mUKniZJoWCB4vzQYPBfy4G2dsbU5JLoHPqKJbwUGxuLlJQUVK5c+ZvPeXh44NatWzhy5Ah++OEHSKVSDiIsmHLlysHCwgIRERFch1LiUWF48bAsi9GjR0MoFGLt2rXZ3pAYiAToUtkE/VxM4WaW2eupsHfbiZjMhMnNTIJ+LqboUsmEkiaik+irlvBSUFBQnh3Dra2tceXKFcTHx8PX1xcJCQnaDbAQqEBcO27fvk31TcWwaNEiBAYGYt++fbleV2NtJEbnSiYY61EWja0NYWckytrG0xMwkAgya5Ykgsw/q7bj7IxEaGxtiLEeZdG5kgk1tyQ6jS5zIrykSpzyYmRkhIMHD2LSpElo2LAh/P394ejoqJ0AC0FV5zRgwACuQynRAgICMGvWLK7D0El79uzBpk2bcOvWLRgb59980kAkQD0rQ9SzMgQApMiUiE2TQ6pgoWBZCBkGEiEDSwMRjLlqCkWIhtBXNOGlwMDAAhX5CoVCrFy5EqNGjULDhg15ubJDK06aFxsbi/j4eLi6unIdis65evUqfvrpJ5w8eRI2NjZFGsNYLEBlEwnczfVQraw+3M31UNlEQkkTKZHoq5rwUkFWnL40btw4bNq0CX5+fjh8+LAGIyu8WrVqITQ0lNe1WLouICAA3t7eVIBfSE+ePEGPHj2wd+9eeHh4cB0OITqBXmUI78TFxeHTp09wdnYu1PM6dOiA06dPY9y4cVixYgVvrjoxNjZG5cqVERISwnUoJRYVhhfe+/fv0a5dOyxZsgQtWrTgOhxCdAYlToR37t+/jxo1ahRp9aB27dq4efMmtm/fjrFjx0Iul2sgwsKjRpiaRYXhhfP582d07NgR33//PQYNGsR1OIToFEqcCO8UtL4pN/b29rh+/TqioqLQqVMnpKSkqDG6oqFGmJqjUChw9+5deHt7cx2KTlAoFOjbty/c3d0xe/ZsrsMhROdQ4kR4R3XVSnGYmJjgxIkTqFixIho3bsz5RbtUIK45T548Qfny5VGuXDmuQ9EJEydORFJSErZs2cLr5rGE8BUlToR3vr5qpajEYjE2bdqE3r17o0GDBnjw4IEaoiua6tWr4/nz50hOTuYshpKK7qcruFWrVuH8+fM4dOgQJBIJ1+EQopMocSK8kpCQgPfv36vtWDnDMJgyZQqWL1+OVq1a4fTp02oZt7DEYjE8PT0RGBjIyfwl2e3bt6kwvAAOHz6MpUuXwt/fH2ZmZlyHQ4jOosSJ8Mr9+/fh5eUFoVCo1nF79uyJI0eOYPDgwdi0aZNaxy4oqnPSDFpxyl9AQACGDx+OY8eOwcHBgetwCNFplDgRXlFHfVNuGjZsiOvXr2PFihX45ZdfoFQqNTJPbqjOSf1SUlIQFRUFLy8vrkPhrejoaHTu3Bl//fWXWrbACSntKHEivKKu+qbcODk54ebNm7h9+zZ69uyJtLQ0jc31NVpxUr979+7B09OT6nVy8enTJ7Rr1w4zZ85E+/btuQ6HkBKBEifCK5pccVKxsLDAuXPnoKenBx8fH8TGxmp0PhVnZ+esGi6iHrRNl7v09HR07twZHTp0wOjRo7kOh5ASgxInwhtJSUl4/fo1qlSpovG59PT0sGvXLrRq1QoNGjTAkydPND6nQCBA3bp1adVJjagwPGdKpRKDBw+GlZUVlixZwnU4hJQolDgR3ggODkb16tUhEom0Mh/DMJg7dy5mzpyJpk2b4vLlyxqfk7br1IdlWVpxysWMGTPw4sUL7Nixg+7vI0TN6DuK8Iam65tyM2jQIOzduxe9evXCzp07NToXFYirz+vXr6FQKOiU2Fc2b96MAwcO4NixYzAwMOA6HEJKHEqcCG9oo74pNz4+Prh06RJ+++03zJ49W2MXBKtWnPhyAbEuU91PR92v/3Pq1CnMmjULp06dok7qhGgIJU6EN7hacVKpWrUqbt++DX9/fwwcOBBSqVTtc9jY2EBPTw/Pnj1T+9ilTUBAANU3fSE4OBgDBgzAwYMH4ezszHU4hJRYlDgRXkhNTcWzZ89QtWpVTuOwsrLC5cuXkZKSgjZt2iA+Pl7tc1Cdk3pQYfh/Xr16hQ4dOmD9+vVo2LAh1+EQUqJR4kR44cGDB6hWrRov+vEYGhriwIEDqF27Nho0aIDo6Gi1jk91TsUnk8kQHByMunXrch0K5xITE+Hn54fx48ejR48eXIdDSIlHiRPhhcDAQF51NRYKhVi+fDl+/PFHfPfdd7h9+7baxqYVp+J7+PAhHB0dYWJiwnUonJLJZOjRowcaN26Mn3/+metwCCkVKHEivBAUFMRZYXheRo8ejT///BMdO3bEv//+q5Yx69Spg6CgIMjlcrWMVxqpCsNLM5ZlMXLkSEgkEqxatYqK5AnREu00zCEkH4GBgRg7dizXYeTIz88PZ8+eRYcOHfDs2TNMmjSpWD+kzMzMULFiRYSFhcHRvSo+pCkgVbBQsCyEDAOJkIGlgQjGYnpfk5uAgAA0atSI6zA4tWDBAgQHB+PKlSta631GCAEYls5FE46lpaXBwsIC8fHx0NPT4zqcXL1+/Rp+fn5o0KAB1q5dW6QfVmlyJULi0nHiTgiMKzhAIBZDxDBg8d+3IQMGcpaFSMDAUl8IZ1MJPC30YSCiRErFzc0NBw4cgKenJ9ehcGLXrl2YMWMGbt26BWtra67DIaRUocSJcC4gIACjRo1CUFAQ16HkKzk5GT179gQA7Nu3r8A1Nm9SZbgTm4bIRCkYAPJCfNeJGIAF4GIqgbelAWyMxIUPvAT59OkTHB0dER8fD6FQyHU4Wnf58mX07NkTly5dQrVq1bgOh5BSh97CEs7xtb4pJ2XKlMHx48fh6OiIxo0b4/Xr13k+Pk2uxOHoJOyJTER4ghQKtnBJE5D5eAULhCdIsScyEYejk5AmVxbjb6Hb7ty5g9q1a5fKpCksLAy9evXCP//8Q0kTIRyhxIlwjm8n6vIjEomwfv16fP/992jQoAHu37+f4+MiEzOw8XE8opKkkLNAcZd2WWQmUVFJUmx8HI/IxIxijqibSuv9dO/evUO7du2wbNky+Pj4cB0OIaUWJU6Ec1xetVJUDMNg0qRJ+OOPP9CmTRv4+/tnfY5lWVx4nYJjz5ORoWChUPNmuIIFMhQsjj1PxoXXKaXu+pbS2DE8NTUVHTp0wODBgzFgwACuwyGkVKMaJ8KpjIwMmJubIy4uTmcvJL19+za6du2KGTNmYNSoUTj1MgVhCRmQaWE3TSwAqpjpwdfeuFQcR2dZFuXKlUNoaGipKYpWKBTo0qULLCwssG3btlLx/5kQPqMzrIRTDx8+hLOzs84mTQBQv359XL9+HX5+fnhn6gDTavUg09LbEZkSCEvIgJ6QQQtbY+1MyqGoqCgYGxuXmqSJZVn89NNP+Pz5M/79919KmgjhAdqqI5zi+mJfdalcuTL2nL8BiZOX1pImFZkSCI5LLxU1T6XtfrqVK1fi0qVLOHjwIC+uIyKEUOJEOKaL9U05SZMrcTmOhUhPn5P5ZUrgxIuUEn/arjQVhh88eBArVqyAv78/TE1NuQ6HEPJ/lDgRTpWUFafTL1MgV3JbLihXsjj9KoXTGDSttBSG37p1C6NGjcLx48dhb2/PdTiEkC9QcTjhjFQqhZmZGT58+AAjIyOuwymyN6ky7IlMLHR/Jk0QMUA/F1NYl8AmmWlpaShXrhw+fvyo0zVx+YmKikLjxo2xdetWtGvXjutwCCFfoeJwonHJMkWO97HFPn2CSpUq6XTSBAB3YtPU3nKgqBQsEBCbhs6VSl7idP/+fVSpUqVEJ01xcXFo164dZs+eTUkTITxFiRNRO9V9bFGJUsSmKyBXsjnex5ahtELfP09jd0SCzt7HliZXIjJRWuzmlurCAohMlCJNrtS5f8v8lPTC8PT0dHTq1AldunTBiBEjuA6HEJKLkvXKSjj1JlWGI8+SsDb0E669/YxXqfKsBpAZShZSJbJ+ZShZQCCEUN8Ar1LluPb2M9aGfsKRZ0l4kyrj+q9SYCFx6eDTAXFZehr+Gt8P5cuao0ePHlyHo1YluTBcqVRi4MCBsLW1xaJFi7gOhxCSB1pxIsWWJlfi9MsUPE3KvIutKKsvqvqg8AQpohKlcDKRoK29Me9XTaISpbyobVJ5eP44kuM+YP3tpxhQtRzX4ajV7du3MW/ePK7D0Ihp06YhJiYG58+fh0DA7695Qko7SpxIsUQmZuDEi8wTZeqo8/n6Prb2DsZwMdUr/sAaEpuu4DqEbBLevUI5eyfEyfm0DlZ87969Q0pKClxcXLgORe02bdqEQ4cO4datW9DX56adBSGk4OitDSmS0nIfm6OjI5YtWwZPT08YGRlhyJAheP/+PXx9fVGmTBmsH9YVaUkJAIDHV05jZfdGmNPECZuHdUJsdETWOEv8auHqjnVY1bMpZjepjD1ThkKWkZ71+bCrZ7G6dzPMaeKEDYPa4W3EIwDA1b/XYtekQdliOrZ0Ko4vm/ZNrOc2LMHFzb8j5NwR/FrfHus2b8GoUaPQrVu3rMdMmTIFLVq00Ln77QICAuDt7V3iOmf7+/tj9uzZOHXqFCwsLLgOhxBSAJQ4kUJjWRanXqYgOC5d4/exqbpin3rJXfJ08OBBnDt3DhERETh+/Dh8fX2xcOFC3HkaA7BK3Ni7BR9ePMU/U0eg/aT5mHHhCdy+a4m/f+oHuUyaNc7Dc0cxeN0+/HI8EO8iHyPo+D8AgDdPQnBwznh0nv47Zl6KgHe3Adgx4XvIpRmo0a47Im5eQlpyIgBAIZcj5MwR1PLr9U2crUZNQbMffoJnq85YfOslfHsPxO+//46HDx9i+/btuHbtGrZu3Yq///5b5xKQklgYHhQUhIEDB+LQoUNwcnLiOhxCSAFR4kQK7WJMqtYusQX+u4/tYkyqdib8yrhx42BlZYWKFSuicePGqFevHmrWrAlGrAcPn3Z4G/4QIWePwL1xS7jUbwahWIzGA8ZAnpGOlw/uZo3TsPcwmJSvAENTc1Rp0gZvwkMBAHcO7YR3twGwr14bAqEQtTv0hkgiwcuH92BSvgIq1aqPh+eOAQAibl6EkVlZVKzqlWfMLFhIFSwMDQ2xc+dOTJw4Ef3798eaNWtga2uruX8sDSlpheEvX75Ex44dsXHjRjRo0IDrcAghhUA1TqRQIhMztLLS9DXVypN9GbHWa56srKyyfm9gYJD1ZwXLQqynj4zPqUj+8A5mFeyyHicQCGBqVRGJsW+zPlamnGXW78X6Bkj68A4AEP/2FYJO7MOtf/7M+rxCLkPSh/cAgFodeiPgwF/w7vo9gv0PoKZf5mm5S1tX4vK2PwAANdr1QJfpy7Oez/4/PgCoV68eKleujNjYWPTs2VMd/yRapVAocO/ePXh7e3MdilokJibCz88PEydOzLaNSgjRDZQ4kQJLkytx4kWK1pMmFdV9bCOrinlx2k74xXZXmfIV8D7qcdafWZZF4vsYmFpa5zuOmVVFNP/hJzQfOjHHz1dt5osjCyfjXVQYnlw7C9/xswAAzYdMQPMhE3J8DvNFfOvWrUNGRgZsbGywdOlSTJ06taB/RV54/PgxrK2tUbZsWa5DKTapVIpu3bqhWbNmmDAh5/93hBB+4/6nD9EZdB9bdhIhA+b/XZw8W3XCk2vnERVwFQqZDNd2rodQogd7r7r5jlO36/cIOPg3Xj4MBMuykKal4sm1s8hIzfx7ivX0Ub1lB+ybNhK21WrBzDr/rTYGmd3ZIyIiMGPGDOzatQs7d+7E0qVLERwcXKy/t7aVlPvpWJbFiBEjYGhoiD/++EPn6swIIZloxYkUyJtUWVafJi4pWOBpohRvU2Wc38dW3kCYtR1W3tEZveavx/GlU5H44S1sXD0w8I9dEIkl+Y5jW7UGusxYgWNLfkXcy2iI9fXhUKMeKtVqmPWYWu174e7hXeg2a1WBYpOzLMqKgdb9+2PKlCnw8sqsiVq4cCG+//573Lt3D3p6/G3z8KXbt2+XiPqmefPmITQ0FJcvX4ZQKOQ6HEJIEdElv6RAjjxLQngCP64WYQC4mUnQuZIJ16FgZUgcMrSQTSa8fY0V3Rpi2tlH0Dcuk+/j9YQMJniWjOPt1atXx/bt21G7dm2uQymyHTt2YNasWbh16xYqVKjAdTiEkGKgrTqSLz7fx8Y1S33NrxwolUpc27UBnq07FyhpArQTlzYkJSUhOjoanp6eXIdSZBcvXsTkyZPh7+9PSRMhJQAlTiRH1apVw+XLlwHw7z42IHPVKSQuPd/HaZqzqQQiDf7jSNNSMadxJUQFXEHLkVMK9BwRkxlXSXDv3j3UrFkTYjG327JF9ejRI/Tu3Rv//PMPqlSpwnU4hBA1oMSJ5OjRo0do1qwZgLzvY1viVwtRAVe0F9j/Rdy9AV8v7q/f8LTQ1+hKnMTACHNuvMCEf6/DrELFAj2H/X9cJYEuN758+/Yt/Pz8sGLFCjRv3pzrcAghakKJE8mXpu5jY1kWSmXRt9uUPCjPMxAJ4GIq4c2KHAPAxVTCi3YN6qCrjS9TUlLQvn17DB06FP379+c6HEKIGlFxOMmRo6Mj/vzzT1y4chX+AQ8glOjj0aWTMKtgix5z18K2ag3smzEaD079C6FEDwKBAD7DJqHpoHF4GXIPJ1f8htjocJhZ26HD5AWoXOc7AMDmYZ3g4OWNZ4E3EPPkIcbvuwKlQoHjS6ciJuwBjMwt0GrUr/Bs3RkA8OT6OZxaORsJ72Ogb1QG3/Ubifo9BmGejzsU0gwYGhoCACIiImBjY8PJv9WbVBn2RCbmuiqnTYxSge/dy8KG4xOH6sCyLKytrXHnzh3Y29tzHU6ByeVydO7cGVZWVvjzzz+p7QAhJUzJeFtKNOaznMXjK2fg2aYzZl15iipN2+DY4l8BAL3mr4dpBVsM/GMX5tx4gaaDxiEx9i22j++L5kMnYOblSLSbMBu7Jg9GSvzHrDHv+x9AlxkrMPvaMxibl8O20d3h1bYrpp8PQ59Fm3F08RS8jw4HABya+xM6T1+OOdef46cD1+BUtxEkBkYYvOYfmJSvgJA3n5CSksJZ0gQANkZiOJlIIOT45yPDKvEi8Dq6t2qKmzdvchuMGrx48QIMw8DOzi7/B/MEy7IYP348pFIpNm7cSEkTISUQJU4kTwoli0o168G9USsIhELU9OuJt5GPcn18sP8BuH3XMvPxAgFc6jeDbRUvhF8/n/WY2h16w8rJHUKRCBE3L8Dc2g51OvWFUCSCjbsnPHzaZ93NJhCJERsdgfSUZBiYmKFilex3tEm5biz1f23tjSEScPtDUiISYtnAjhgxYgR69eqF7t27IyoqitOYikO1TadLycfvv/+Oa9eu4d9//9XZgnZCSN4ocSJ5UgIoY/HfHWsSfQPIM9KhkMtzfHz829cIPX8Mc5o4Zf16HnwHyR/fZz3G1Kpitse/Cg3K9vjgU/8iJS4WANBv2V8Iv3EeS/xqYvPQjnjxxaW5wH/3sXHNQCRAewdjiDn6jhILgPYOxjDSE2PgwIEIDw9HrVq1UL9+fYwfPx4fP37MfxCe0bXC8AMHDmDVqlU4efIkTEy47zFGCNEM6hxO8pRfHvD1aoCZlQ1q+vVA15kr83jOf783rVARlWo3xJAN/+b4WLtqNTFg5U4oZDLc2rcVe38dil9PPYCqGlvIo9UIF1M91LCQaf0SZLEAqGGhn+3yY0NDQ0ybNg1Dhw7F3Llz4e7ujl9++QU//vgj9PV148RdQEAAFi5cyHUYBXLz5k2MGTMGZ8+e1amtRUJI4dGKE8mTMJ/tJ+Oy5fHp9YusP9do1wNhV88g4uZFKBUKyDLSEX3vBhLfv8nx+VUat8bHF08RdGI/FDIZFDIZXj26j9joCMhlUtz3/xfpyUkQisXQMzYGwwj+P68lUhPjkZGSpL6/rBr4VDRCFTM9ra08iQVAVTM9+FQ0yvHzlpaWWLt2LW7cuIFbt27Bzc0Nu3btKtZpRm2QSqV48OAB6tSpw3Uo+YqMjES3bt2wY8cO1KhRg+twCCEaRokTyZOhiMmzT1GzH8bj4tYVmNPECVd3rINZhYr4fsVOXN72B+a3cMcS3xq4umMt2Fx+UOsZGeOH9QcQcvYwFrWpjgWtq+H0qrmQyzIAAPdP7seS9rUwu3ElBPz7N3ot2AAAsKzkghptu6B5DXeYmZnhzZucEzNtYxgGvvbGqGGhr/HkSbXS1NbeON86IDc3Nxw+fBi7du3C6tWrUbduXVy6dEmzARbDgwcP4OzsDGNjY65DydPHjx/Rrl07zJkzB23btuU6HEKIFlA7ApIvbd3HVlh8v48tMjEDJ16kQK5k1Xo5slyaAbFQgK4uZbNtzxWUUqnE/v37MW3aNFSrVg1LlixB1apV1RegGqxduxYhISHYvHkz16HkKi0tDS1atEDTpk2xaNEirsMhhGgJrTiRfPH13jO+xqXiYqqHkVXN4WySeS1LcauxGGRep2IjkmNdryYwSo0r0jgCgQC9e/dGWFgYmjdvjqZNm2LEiBF49+5dMSNUH74XhiuVSgwcOBAODg5YsGAB1+EQQrSIEieSL03fx1YUunIfm4FIgC6VTdDPxRRuZpm9nqRpnws1hogBhAzgZiZBPxdT/FDbAYP798WwYcNQnAVjPT09TJw4EeHh4TA2Nka1atUwd+5cpKamFnlMdeF7x/ApU6bg3bt32L59OwQCehklpDSh73iSL03fx1YUunYfm7WRGJ0rmcA26ipeXToCOyMR9IQMhAygJ2AgEWTWLEkEmX8WMplbkXZGIjS2NsRYj7LoXMkE1v/vCD59+nS8ffsWW7duLXZsZcuWxe+//467d+/i8ePHcHV1xdatW6FQaOaqnfzExcUhNjYW7u7unMyfn/Xr1+P48eM4cuQI9PQKv1VKCNFtVONECuTIsySEJ0h5kUAxYOFmpofOlXSvV86AAQPQoEEDjBo1CgCQIlMiNk0OqYKFgmUhZBhIhAwsDUQwzqe6/OHDh2jevDnu3bsHR0dHtcUYEBCASZMmISEhAcuWLUObNm202oTS398fK1aswPnz5/N/sJadOHECw4YNw40bN1C5cmWuwyGEcIBWnEiBeFsacH6liIo0PR2P/ffh8+fCbXlxTaFQ4NSpU2jfvn3Wx4zFAlQ2kcDdXA/VyurD3VwPlU0k+SZNAFC9enVMnjwZgwcPVmt7gXr16uHq1auYN28efvzxR7Ru3RrBwcFqGz8/AQEBvKxvCgwMxODBg3HkyBFKmggpxShxIgXCl/vYhAzgaCxE8JVzcHV1xbZt2zjbUiqs27dvo2LFimptkDhp0iRkZGRg7dq1ahsTyGyr0LlzZzx69AidO3dGmzZtMGjQILx+/Vqt8+SEj4XhL168QMeOHbF582bexUYI0S5KnEiB8eE+NpGAQY8qFbB//34cPHgQ27dvh5eXF06ePFmsQmltOHHiRLbVJnUQCoX4+++/MXfuXISHh6t1bAAQi8UYM2YMIiIiYGNjAy8vL0yfPh1JSZppPKpUKnHnzh1eJScJCQlo164dJk+ejC5dunAdDiGEY5Q4kQLjy31s+qLMAOrVq4crV65g4cKFmDx5Mnx8fHDv3j1ugisATSROAODi4oLZs2dj4MCBkOdyh2BxmZqaYuHChQgODsbr16/h6uqK9evXQyaTqXWeiIgImJmZwcrKSq3jFpVUKkXXrl3RsmVL/PTTT1yHQwjhAUqcSKFk3sem+a7YX8vpPjYgc0upY8eOCAkJQd++fdGxY0f06dMH0dHR2g0wH8+fP0dsbCy8vb01Mv7o0aNhZGSEZcuWaWR8FTs7O/z99984deoUDh06BA8PDxw9elRtq318akPAsiyGDh0KExMTrFixgutwCCE8QYkTKTS+3ccGACKRCMOGDUNkZCSqVq2KunXrYsKECYiLK1qTSHU7ceIE2rVrp7GePwKBAH/99RdWrFiBkJAQjczxpZo1a+LcuXP4448/MH36dDRt2hR37twp9rh8KgyfM2cOnjx5gj179kAo5HezVUKI9lDiRAqNr/exAYCRkRFmzpyJx48fQyqVws3NDYsXL0ZaWppmA82HprbpvmRvb4+lS5diwIABkEqlGp0L+P/Xga8vgoODMWDAAHTp0gV9+vTBs2fPijzm7du3ebHitH37duzYsQPHjx+HoaEh1+EQQniEEidSJAzDoIWtMTo6lslq5KhOqgaQHR3LoIVtwZKmL1lZWWHdunW4ceMG7t69C1dXV2zfvp2TE3gpKSm4efMmWrVqpfG5Bg0aBDs7O8ydO1fjc6mIRCIMHToUERERqFKlCurUqYNJkyYhPj6+UON8/vwZ4eHhqFGjhmYCLaDz589jypQp8Pf3502tFSGEPyhxIsWiqfvYnE0lGFnVvEiX2H7Jzc0NBw8exL59+7BlyxbUrFkTp0+f1uoJvHPnzqF+/fowMdF8w06GYbB582Zs2bIFAQEBGp/vS0ZGRvjtt98QGhqK5ORkuLm5YeXKlcjIyCjQ8wMDA1GtWjXo63PXET40NBR9+/bFgQMHeNu5nBDCLUqcSLHldB9bYe+2+/o+ti6VTGAgUt+XZ8OGDXH9+nXMnTsX48ePR6tWrRAUFKS28fOijW26L1lbW2PNmjUYOHAgJ1uU1tbW2LRpEy5duoQLFy6gSpUq2L9/f77JKteF4W/evIGfnx/++OMPNGnShLM4CCH8RleuELVLkysREpeOqEQpYtMVkCtZiBgGLFiwyFxVkkllUAAw0JPAUl8IZ1MJPC301Zos5UYmk2Hr1q2YM2cOWrRogfnz56v1ypIvKZVK2NjY4ObNm1rvNt27d29YW1tj5cqVWp33axcvXsSkSZMgkUiwfPlyNGrUKMfH9ejRA507d0a/fv20HGHmdmqTJk3QvXt3TJs2TevzE0J0ByVORONyuo8tIuwR/pj3Gy6eOsFZXMnJyfj999+xZs0aDBo0CNOnT0fZsmXVOsedO3cwePBgPHr0SK3jFkRcXByqV6+OPXv2oFmzZlqf/0tKpRK7d+/G9OnTUadOHSxevBiurq7ZHmNnZ4fLly/DyclJq7HJ5XJ06tQJNjY22Lx5s1bv5SOE6B7aqiMal9N9bHUrWeNR0F1O4ypTpgxmz56N0NBQpKamws3NDcuWLUN6erra5tD2Nt2XLCwssHnzZgwePBjJycmcxKAiEAjw/fffIzw8HPXq1UPDhg0xduxYfPjwAQAQExOD9PR0ra/KsSyLcePGQaFQYP369ZQ0EULyRYkT4USFChWQmpqqsas7CsPa2hobN27E1atXcePGDbi5uWHnzp1quTiXy8QJANq3bw8fHx9MmjSJsxi+ZGBggClTpiAsLAwCgQBVqlTBokWLcPXqVXh7e2s9cVm2bBlu3ryJ/fv3QywWa3VuQohuosSJcIJhGDg5OeHp06dch5KlSpUqOHLkCHbv3o3169ejdu3aOHfuXJHHi4mJwYsXL9CgQQM1Rll4K1euxJkzZ3Dq1ClO4/hS+fLlsXr1aty6dQv37t3DiBEjoK+vr5ZktaD27duHtWvX4uTJk1o58UgIKRkocSKccXJyQlRUFNdhfKNRo0a4efMmZsyYgdGjR6NNmzYIDg4u9DgnTpyAr68vRCKR+oMsBBMTE2zduhXDhg3Dp0+fOI3lay4uLjh48CCcnJzw+PFj1KlTBxcuXND4vNevX8e4ceNw4sQJ2Nraanw+QkjJQYkT4YyzszMvEycgc0WsW7duePz4MTp27Ii2bdtiwIABePHiRYHH4Hqb7kstWrRAly5d8OOPP3IdyjfkcjmioqJw8+ZNTJ06FcOHD0e7du0QGhqqkfkiIiLQvXt37Ny5E56enhqZgxBSclHiRDjj7OzMq626nIjFYowZMwYRERFwcHBArVq18Msvv+TbFfvz58+4cuUK2rRpo6VI87dkyRLcuXMHBw8e5DqUbB49egRbW1uYm5ujR48eePz4MVq3bg0fHx8MGzYMb9++VdtcHz58QLt27bBgwQJe/b8hhOgOSpwIZ/i84vQ1ExMTzJs3Dw8fPkR8fDzc3NywYsWKXLtiX7p0CbVq1YK5ubmWI82doaEhtm/fjjFjxuD9+/dch5Pl9u3b2S721dPTw08//YTw8HCYmZnBw8MDs2fPRkpKSrHmSUtLQ8eOHdG7d28MGTKkuGETQkopSpwIZ3QpcVKxsbHBli1bcPnyZVy+fBnu7u7Ys2fPN0XNx48fR4cOHTiKMncNGzbEoEGDMHLkSK1eO5OX3DqGm5ubY9myZQgMDERkZCRcXV2xZcsWyOXyQs+hVCrRv39/VK5cGfPmzVNH2ISQUooaYBLOKJVKGBkZIS4uTmdvoL9y5QomT54MhUKBpUuXokWLFmBZFnZ2drhw4QLc3Ny4DvEbGRkZqFOnDn755Rd8//33XIeDqlWrYvfu3ahZs2aej7t37x4mTZqEDx8+YNmyZfD19S1w+4Kff/4ZgYGBOHPmDPT0inf/ISGkdKPEiXCqSpUqOHDgADw8PLgOpchYlsWBAwcwdepUuLq6YvDgwZg+fToiIiJ421Dx/v37aNOmDYKCgjg9VZaYmIiKFSsiISGhQKcPWZbF8ePHMWXKFNjY2GD58uX5Jlxr167FunXrcPPmTV5tnRJCdBNt1RFO6UKBeH4YhkHPnj0RFhYGX19f/PDDD5BIJHj9+jXXoeWqZs2aGDduHIYMGcLplt3du3dRq1atArdsYBgGHTt2xMOHD9GjRw+0a9cOAwYMwMuXL3N8/LFjx7Bw4UL4+/tT0kQIUQtKnAindLHOKTcSiQQ//vgj3Nzc4OXlhRo1amDq1KlITEzkOrQc/frrr/j06RM2bdrEWQy3b9/Osb4pPyKRCCNHjsw67VizZs1v/q3v3buHoUOH4ujRo6hUqZI6wyaElGKUOBFOlaTECQDev3+P6OhobN++HQ8ePEBsbCxcXV2xatUqSKVSrsPLRiwW4++//8aMGTM4W/ULCAjIdqKusMqUKYN58+bhwYMHeP/+PVxdXbF27VpERkaiU6dO2LJlC+rWravGiAkhpR0lToRTJS1x8vf3R6tWrSCRSGBra4utW7fi/PnzOHv2LKpUqYJ9+/Zp9VqR/FStWhXTpk3D4MGDoVAotDo3y7LftCIoKltbW2zbtg1nz57FoUOH4OHhAV9fX3Ts2FENkRJCyH8ocSKc4uu1K0WVUxuC6tWr4+TJk9iyZQuWLVuG+vXr4/Lly9wEmIPx48cDAFatWqXVeZ89ewY9PT21Fqe7u7uDZVn4+fnhzp07aNy4MW7fvq228QkhhBInwikHBwe8efOGd9tYRZGRkYELFy7A19c3x8/7+Pjgzp07mDBhAgYPHoz27dvj0aNHWo7yW0KhEH/99RcWLlyIx48fa23e4m7TfY1lWQwdOhTm5uY4cOAA7t+/jyFDhqB79+7o1asXoqOj1TYXIaT0osSJcEosFsPOzg7Pnz/nOpRiu3LlCjw8PFCuXLlcHyMQCNCnTx88efIELVq0QPPmzTF06FDExMRoMdJvOTk5Yf78+Rg4cCBkMplW5ixqYXhufvvtN0RGRmLXrl0QCoUQCoUYPHgwwsPDUb16dXh7e2PixIm8u+iYEKJbKHEinCspdU6FudRXT08PEyZMQEREBMqVKwdPT0/MmDEDSUlJGo4ydyNGjEDZsmWxePFircynzhWnbdu2Yc+ePTh27Ng3zVSNjIwwY8YMPHr0CGlpaXBzc8Py5cuRnp6ulrkJIaULJU6EcyUhcVI1ZizsNStmZmZYvHgx7t+/j9evX2edCuNi65JhGGzduhVr1qzB/fv3NTpXRkYGHj58iNq1axd7rHPnzmHatGnw9/eHpaVlro+zsrLChg0bcPXqVVy9ehVVqlTB3r17eVWsTwjhP0qcCOdKQuL0+PFjsCyLatWqFen59vb22L59O86cOYMTJ06gWrVqOHDggNabU9ra2uL333/HgAEDcr3AWB2Cg4Ph6uoKIyOjYo0TEhKCfv364cCBAwW+3qZKlSo4duwYtm/fjhUrVqB+/fq4evVqseIghJQelDgRzpWEk3WqbbriXrHi5eWF06dPY/369Vi4cCEaNGiAa9euqSnKgunfvz+cnZ0xa9Ysjc2hjjYEMTExaN++PVavXo3GjRsX+vlNmzZFQEAAfvrpJwwYMACdOnXCkydPihUTIaTko8SJcK4kXLtSmPqmgmjVqhUCAwMxbtw4fP/99+jUqRPCwsLUNn5eGIbBpk2bsH37dty8eVMjcwQEBBSrMDw5ORl+fn4YPXo0evfuXeRxBAIB+vbtiydPnqBx48Zo3LgxxowZg9jY2CKPSQgp2ShxIpyrVKkSXrx4AblcznUoRRIXF4eQkBA0a9ZMreMKBAL069cPT548QZMmTdCkSROMGDECb9++Ves8ObG0tMT69esxaNAgpKamqn384hSGy2Qy9OzZE/Xq1cOUKVPUEo++vj4mTZqEJ0+eQCKRoGrVqliwYAE+f/6slvEJISUHJU6Ec/r6+rCyssKrV6+4DqVITp06BR8fH+jr62tkfH19ffz8888IDw+HiYkJPDw8MGvWLCQnJ2tkPpWuXbvC29sbv/76q1rH/fDhA+Li4gpck/QllmUxZswYAMC6deuKvTX6NQsLC6xcuRIBAQF48OAB3NzcsH37dq13VSeE8BclToQXdLlAXN3bdLkpW7Ysli1bhsDAQERHR8PV1RXr16/XaN+lNWvW4PDhw7hw4YLaxgwICIC3tzcEgsK//CxZsgR3797F/v37IRKJ1BbT15ycnLB//37s378fW7ZsQe3atXHu3DmNzUcI0R2UOBFe0NXESSaT4ezZs2jXrp3W5nR0dMTOnTvh7++fdS/boUOHNHICz9zcHH/++Sd++OEHJCYmqmXMohaG7927Fxs2bMDJkydRpkwZtcSSnwYNGuD69euYOXMmRo8ejbZt2+Lhw4damZsQwk+UOBFe0NWTddevX4ezszOsra21PnfNmjVx7tw5rF69GnPmzMF3332HGzduqH2etm3bom3btpg4caJaxitKYfjVq1cxfvx4nDhxAjY2NmqJo6AYhkG3bt3w6NEj+Pn5oWXLlhgyZAjn3d4JIdygxInwgq6erNPWNl1uGIZBmzZtEBQUhJEjR6JPnz7o2rUrwsPD1TrP8uXLcfHiRRw/frxAj0+WKRCdJMWT+Aw8+pSOJ/EZiE6SIilDjrt378Lb27vAc4eHh6NHjx7YvXs3qlevXtS/QrFJJBKMGzcO4eHhKF++PDw9PfHbb79pvNaMEMIvDKvtDnuE5CAkJAR9+/ZFaGgo16EUipubG/bu3YtatWpxHQoAIC0tDWvWrMHSpUvRs2dPzJo1C1ZWVmoZ+8qVK+jTpw8ePnwICwuL7PPKlQiJS0dUohSx6QrIlSxEDAMW/728MGAgUyqRlpoCZ0tzOJtK4GmhDwNR7u/fYmNj0aBBA8yYMQODBw9Wy99DXV68eIEZM2bg/PnzmD17NoYMGaLRuitCCD9Q4kR4ITU1FeXLl0dKSkqRioa5EBERgebNm+P169dqP91VXHFxcViwYAH+/vtv/Pjjj/j5559hbGxc7HEnTpyIN2/e4J9//gEAvEmV4U5sGiITpWAAyAvxaiJiABaAi6kE3pYGsDESZ/v858+f0bx5c7Rp0wZz584tduyaEhgYiMmTJ+Pdu3dYunQp/Pz8ePf1QAhRH0qcCG/Y2Njgzp07sLW15TqUAlmxYgXCw8OxadMmrkPJ1bNnzzB9+nRcvnwZs2bNKvaqSFpaGmrWrInf5i2AQe1WeJokhYIFivMiwgAQMoCTiQRt7Y1hIBJAoVCgR48eMDY2xt9//837RIRlWfj7++OXX36BpaUlli9frpZ7+AorWabAhzQFpAoWCpaFkGEgETKwNBDBWKwbb0gI4TtKnAhvNG7cGPPmzVN7I0lN8fHxwYQJEwp9sS8X7t27h19++QVv377F4sWL0bFjxyInIyfvhuJumgEMy5hACfUlNEIGEAkYtHcwxvrZvyI4OBhnzpyBRCJR2xyaJpfLsW3bNsyePRs+Pj5YsGABHBwcNDZfQbdI5SwLkYCBpb6wQFukhJDcUeJEeGPw4MFo1KgRhgwZwnUo+UpISIC9vT3evXsHQ0NDrsMpEJZlcfr0afzyyy8wMzPDsmXLCnW6jWVZXIxJRXBcOmRKDQaqkOPR6X+xanRflC1bVoMTaU5KSgqWLVuGtWvXYujQoZg6dSrMzMzUNr4mt0gJIXmjtxyEN3Spl9OZM2fQpEkTnUmagMwTeL6+vggODsYPP/yA7t27o0ePHoiMjMz3uSzL4tTLFM0nTQAgFMHTrxduJ4s10ptKG4yNjTFnzhw8fPgQnz59gpubG1atWgWpVFqscdPkShyOTsKeyESEJ2RukxYmaQIyH69ggfAEKfZEJuJwdBLS5Jr+n0pIyUGJE+ENXUqcuG5DUBxCoRCDBw9GREQEatWqhQYNGmDs2LF5Xmx7MSYVYQkZmk+a/k8BBmEJGbgYo/578rTJxsYGW7ZswYULF3DmzBlUrVoV//77b5ESwsjEDGx8HI+oJCnkxawrAzKfL2eBqCQpNj6OR2RiRjFHJKR0oMSJ8IauJE4KhQKnTp2Cn58f16EUi6GhIaZOnYqwsDAIhUJUqVIF8+fP/+ZS38jEDO2sNH1FpgSC49JLxA90Dw8P+Pv7Y+PGjZg/fz6+++473Lp1q0DPZVkWF16n4NjzZGQoWCjUvAinYIEMBYtjz5Nx4XWKzq7yEaItlDgR3lB1D+f7C/ft27dha2sLOzs7rkNRi/Lly2PVqlW4c+cOHj58CFdXV/z555+Qy+VIkytx4kWK1pMmFZkSOPEipcRsJbVs2RKBgYEYMWIEevbsiR49euT5ZkGbW6SqRPXUS0qeCMkLJU6EN8zMzKCnp5fnlhEfHD9+XCdO0hWWk5MT9u3bh8OHD2Pnzp3w8vLCtoBIyJXc/hCVK1mcfpXCaQzqJBQKMXDgwKyt0vr16+Onn35CXFzcN4/V9hapTIkSsUVKiCZR4kR4RReuXtHl+qaC8Pb2zuz7tGIt4oWGat8aKiwFCzxNlOJtqozbQNTMwMAAU6dOxePHjyGXy+Hu7o6lS5ciPT0dAG2REsJXlDgRXuF7ndOzZ8/w4cMH1K1bl+tQNIphGEhca0OsZ8B1KAAyk6eA2DSuw9AIS0tLrF27FtevX8etW7fg7u6OHXv30xYpITxFiRPhFb4nTidPnkS7du105lqYokqTKxGZKC32yS11YQFEJkpL9A9yNze3rG3Su6kSfE7ndsWnpG2REqIuJfvVn+gcvidOJbW+6Wshcelq7An+rb/G9kLg8X8K9RwGmXGVdE616sOhTiMIxdx2TC+pW6SEFBclToRXVCfr+Cg5ORk3b95Eq1atuA5F46ISpYVurFgYg9fuQ+0OvQv1HDmbGVdJdyc2jfO6MpWSvEVKSFFR4kR4hc/F4efPn0eDBg1QpkwZrkPRuNh0RZGfq5DL1RhJdsWJSxfQFikh/Ff0a9IJ0YBy5cpBLpfj06dPvLunrLRs0yXLFDm2IIgJe4CDc39C3KtncG3oA4YRoJx9ZTjXa4p9M0ahYe+huL57I5zrNUPHXxZi34zReB0aBIVCDgcvb3SZvhymVjYAgM3DOqFmu+6o2+V7BB7bi7tHdsG+eh3cPbIbBmVM0WnqErh91/KbGORKFikyJYzFJfM9n6a3SItCtUVaz0p3rhciRJNK5qsP0VkMw/By1UmpVOLkyZM63y28ID6kKSBisv/4lsuk2PXzINTu0Bu/XYqEV5uueHzJP+vzKXGx+JyYgCkn76PrjN+hVCpRp1Mf/HIyCL/6B0Osr49jS37Ndc5XoUEo5+CMmRfD0WTgWByc81OOTRhFDIPYNM2taHFN01ukRVkNLC1bpIQUFCVOhHf4WCB+7949lCtXDpUrV+Y6FI2TKliwX20WvQq5B6VCjoZ9hkMoFsOjRXvYetTM+jzDCNBy5C8QSfQg1jeAkVlZeLToAImBIfSMjNF8yAREB97MdU6zCrbw7vo9BEIharXvheSP75ES920jVBYspHwpANKA/LYiE97FYNfPgzDfxx1zm7vi6OIpUCqVuPjn71jSribmt6iC/TPHID05CQAQ/+YlptYqj7tHdmFxuxr4c0QXBB7bi40/+MF/5SzMaeqMpe1rI/zG+WLFRUhpQlt1hHf4mDiV9KaXX1LksNKT9PE9TCytwXyxEmVqVTHr90bmFhDr6Wf9WZr2GSd/n4mImxeRlpwAAMhITYFSoYBAKPxm/DLlLLN+LzHI3BLK+JyKr6vJ0tLTsWHTXmQ8fwSJRJLtl1gs/uZjuX28oI8Vi8XZ/s6alNsWqYpSocDf4/vBqW4j/DI/EIxAiJjHwQg6theBx/7BsM2HYVS2HA7MHIujS35Fr/nrs577LPAWJh68AYYRIOTsEbwKDUKt9r0x82I47hzagYNzfsLUMw9z/buW9C1SQgqDEifCO05OTrhy5QrXYWRz/PhxrFmzhuswtEKYww/PMuUskRT7FizLZv1wTXwfAwtbx8wHfPWca7s24MOLKIzecRplylnhTfhDrOnjU+w70ERCIdxcnMCYiiCTySCVSrN+paamIiEhIdvHVL++fmxBPy6TySAWiwuVlBX14yhnC2WlOoBQnOPf/VVoEJI/vIPvT7MhFGW+dDvWrI/zG5eiUf9RKPv//xdtxs3AHz0bo/vs1VnPbTliMiQGRll/Vq3wAUCt9r1wdNEvSImLRZlyVjn/u/9/i9SY4xYJhPABJU6Ed5ydnbFt2zauw8jy+vVrvHz5EvXr1+c6FK2QCBkwYIAvtuvsPeuCEQpxa9+fqNd9MMKvn8Pr0PuoXPu7HMeQpqZArKcP/TKm+JwYjwubl6snNrEEbVu1RGUT7fwAZ1k2W3JV1AQsp4+lpKRk+7iefQYs7WpCmEvilPg+BmbWtllJk0rSh3cwt7bN+rOZtS2UcjlSPn3I+tiXq4NAwVf4sv4dSvgWKSGFQYkT4R2+bdWdPHkSvr6+EIlKx7dLeQMh5F+tDInEEvRfvh2H5v6EM2vmw/W7FnBv3BpCiV6OY3zXbwT+mTYC833cUKZ8BTTuPzpbMXlRyVkWlgba+//AMEzWqpCmPfqUjjOvUiDN5eS/qVVFJLyLgUIuz5Y8mZSvgPi3r7P+nPAuBgKRCMZlyyMp9k3mB4u53cgi5y1cQkqj0vGTgOgUa2trJCcnIzk5mRc9k06cOIF+/fpxHYbWlBELIRIwUHy1wmBbtQZ+/Ody1p/XDWgD9yatUbnOd5h6OiTbY03KV8DwLUezfaxe94FZv//yc7U79kHtjn2yPXZR0AfkRCRgSmydTU5bpF+y86iFMuWscGbNPLQc+UtmjVPYA3i17YIr29fA7bsWMDK3wNm18+HZqvM3K1PFwRQgPkJKi5L5CkR0GsMwcHJy4kVLgs+fP+PKlSto27Yt16FolaX+twXc0YE3kPzxPRRyOQKP/4N3kY/h2tCH87hKiv+2SHMmEAox8I9diHv1DIvb1cRiXy+EnD2C2p36oaZfT2we2hHL2teBSKKPjlMWqTU2BgwkQkqcCAEAhi1utSYhGtClSxf069cP3bt35zSOEydO4Pfff8elS5c4jUPbAt5/xrW3n7P1FLpzcAfObVgMaVoqylZ0RJtx0+HeuLXWYhIxQGNrwxLbiDFZpsDGR/G8uW7lS0IGGFWtbIld7SOkMGirjvASX5pglqY2BF/ytNDH1befs33Mu9sAeHcbwFFEmXU2nhb6+T5OV+W2RcoHJXmLlJDCou8Ewkt8KBBnWbbUJk4GIgFcTCW8uf6DAeBiKoGBqGS/ZPF1K5KvcRHChZL9KkR0Fh8Sp+DgYBgaGsLNzY3TOLjibWkAvpS1yDLSIY55wnUYGudsKoGIJ//mKiImMy5CSCZKnAgv8SFxKq2rTSo2RmI4mUg4T56EDFAmIwFjv++FDh064NGjR9wGpEGeFvrg20ZdSd8iJaSwKHEivGRra4uPHz8iLS2NsxhKe+IEAG3tjSEScJs5iQQMRnxXFU+ePIGPjw+aN2+OIUOG4PXr1/k/WcfQFikh/EffDYSXhEIhHBwcEB0dzcn87969Q0REBBo3bszJ/HxhIBLA7NUDyNI/5/9gDRALgPYOxtAXCaCnp4cJEyYgIiIClpaW8PLywtSpU5GQkMBJbJrCpy1SIQPUszTgOgxCeIUSJ8JbXJ6s8/f3R+vWrTPvECullEolZsyYgTljfoCLvgLaPlQlFgA1LPThYpq9O7mZmRkWLVqEBw8eIDY2Fq6urli5ciUyMjK0G6CG8GmL1MlUAmuj0vs9QEhOKHEivMVlnVNp36ZLTk5G165dcfXqVdy5cwe9ajiiipme1pInsQCoaqYHn4pGuT7G1tYWW7duxaVLl3Dp0iW4u7tj9+7dUCpzubNEh/Bli9TXzpjTGAjhI0qcCG9xlThlZGTgwoUL8PX11frcfBAdHY0GDRrAysoK58+fh6WlJRiGga+9MWpY6Gs8eVKtNLW1NwZTgGs+qlWrhmPHjuHvv//GmjVrULt2bZw9e1azQWqYgUiA9g7GWl/lU/lyi5QQkh19VxDe4ipxunz5MqpXr45y5cppfW6uXbx4EQ0bNsTo0aOxcePGbJfbMgyDFrbG6OhYBnpCRu1bSUIG0BMy6OhYBi1sC5Y0falJkya4desWZsyYgbFjx6JVq1YICgpSb5Ba5GKqp5VE9Wu5bZESQjJR4kR4y8nJiZPEqTRu07Esi7Vr16Jv377Ys2cPRo8enWvi4mKqh5FVzeFsktlzSKlUFHNyJeQZ6XAyEWNkVfNi/cBmGAbdunXDo0eP0LVrV/j5+aFfv3549uxZ8WLkiE9FI95tkRJS2lHiRHjLwcEBb968gVQq1dqcpbFbuFQqxfDhw7Fp0ybcvHkTPj75X9xrIBKgS2UTtC0rR/jl0xAyKHTjRhGTucrkbq6Pq8unQH7vjNqOvYvFYowaNQqRkZFwdXVFnTp1MGHCBHz8+FEt42sLn7dICSmtKHEivCWRSFCxYkW8ePFCa3M+fvwYLMuiWrVqWpuTS7GxsWjRogU+fvyImzdvonLlyoV6/sG/NsEw6jbGepRFY2tD2BmJsrbx9AQMJILMH8gSQeafVdtxdkYiNLY2xFiPsuhcyQSTRwzCzJkzIZfL1fr3MzY2xqxZs/D48WNIpVK4u7tj0aJF+PyZm/YKRcH3LVJCShuGZVm+NaolJEubNm3w008/aa1Qe/HixYiJicGaNWu0Mh+X7t+/j86dO2PgwIGYPXs2BILCvY+SSqVwcHDAhQsXULVq1WyfS5EpEZsmh1TBQsGyEDIMJEIGlgaiHC+LZVkWLVq0QN++fTF06NBi/b3yEhkZiWnTpuHWrVuYPXs2Bg0aBJFId+46T5MrcfplCp4mSaFgUbwu4ywLkYCBk6kEbe2MqcklIQVE3ymE17RdIF5atukOHDiA1q1bY/ny5Zg7d26hkybVGB4eHt8kTQBgLBagsokE7uZ6qFZWH+7meqhsIskxaQIyV1UWLlyIOXPmID09vdCxFJSLiwsOHDiAgwcPYufOnfDy8sKxY8egK+8fVVuk/VxM4WYmKfIWKRRyJEUEo5+LKbpUMqGkiZBCoBUnwmsrV67E8+fPsWrVKo3P9fHjRzg5OeH9+/fQ1y+Zd3MplUrMmjULO3fuxOHDh1GzZs0ij1WvXj3MmDEDHTp0UFt8nTp1QtOmTTFx4kS1jZkblmXh7++PKVOmoGzZsli6dCnq16+v8XnVKU2uREhcOiISMhD1/hOMTUwhYhiwYMEi88oUBgzk/19dstQXwtlUAhs2FdXdXfDs2TOYm5tz/dcgRKfozho1KZWcnJxw/vx5rcx16tQptGjRosQmTcnJyfj+++8RFxeHO3fuwNLSsshjBQQE4OPHj2jXrp0aIwQWLFiAFi1aYOjQoTAxMVHr2F9jGAZ+fn5o27YtduzYgR49esDb2xsLFy6Em5ubRudWFwORAPWsDCF6HYalYwbhVtCDAm6RGqJdu3bYsWMHxo8fz1n8hOgiWp8lvKbNa1dK8jadqqmlpaUlLly4UKykCQBWrVqFsWPHQigUqinCTB4eHmjdujVWrFih1nHzIhQKMXjwYERERMDb2xuNGjXCqFGj8O7dO63FUFxBQUGoXbt2obZIR44ciU2bNunMNiUhfEGJE+G1ypUr4/nz51AoitkrKB8ymQxnz55V+woKH6iaWo4aNQqbNm3K1tSyKN68eYNTp05h8ODBaoowuzlz5mDNmjVabx1gYGCAKVOm4MmTJzA0NES1atUwa9YsJCcnazWOoggKCkKtWrUK9RzVBdbXrl3TREiElFiUOBFe09fXh6WlJV69eqXRea5fvw4XFxdUqFBBo/No09dNLceMGaOWo+YbN25E3759YWZmVvwgc1C5cmX07t0bixYt0sj4+bGwsMDvv/+OwMBAREdHw9XVFevWrYNMJuMknoIIDAxE7dq1C/UchmEwYsQIbNq0SUNREVIyUXE44T0fHx9MmzYNLVu21NgcEydOhLm5OWbOnKmxObRJKpVi7NixuHXrFo4ePVro/ky5ycjIgIODAy5duoQqVaqoZcycvH37Fh4eHggODoadnZ3G5imI4OBgTJkyBdHR0ViwYAF69OjBq15HMpkMpqamiI2NhbFx4S7ljY+PR6VKlRAVFVUqrxgipChoxYnwnjauXilJ9U2qppaxsbFFamqZl3379sHLy0ujSRMAWFtbY9iwYZg7d65G5ymIGjVq4MyZM9iwYQMWL16MevXq4fLly1yHlSUsLAwODg6FTpoAwNzcHJ07d8b27dvVHxghJRQlToT3NF0gHhERgdTUVNSoUUNjc2hLcHAwvL290axZMxw6dAhlypRR29gsy2L16tX48ccf1TZmXqZMmYIjR44gIiJCK/Plp2XLlrh37x4mTJiAH374AX5+fnj48CHXYRVpm+5Lqu06pVKpxqgIKbkocSK8p+kmmKrVJj5tvxTFgQMH0KpVKyxbtgzz5s0rUlPLvNy6dQuJiYla6+Jubm6OCRMm8Gr7VCAQoE+fPggLC0Pr1q3RsmVLDB48WOM1eHkpSmH4l+rXrw9DQ0NcunRJjVERUnJR4kR4T9OJ0/Hjx9XaxFHblEolZs6cicmTJ+Ps2bPo0aOHRuZZvXo1xo0bp/aELC/jx4/H1atXERQUpLU5C0JPTw/jx49HREQEbGxsUKNGDUyZMgXx8fFajyUwMLBYiZOqSHzjxo1qjIqQkouKwwnvpaSkwNLSEikpKWr/oZ2QkAB7e3u8e/cOhoaGah1bG1RNLT9+/IhDhw4Vuz9Tbl6/fg1PT088f/5c440pv7Z27Vr4+/vD399fq/MWRkxMDGbPno2jR49iypQpGDNmjFYaqSoUCpiamiImJgampqZFHicpKQkODg4ICwsrUSdLCdEEWnEivGdsbAxTU1O8fftW7WOfOXMGTZo00cmkKTo6Gg0bNkT58uVx8eJFjSVNQGYLgv79+2s9aQKA4cOHIywsjNf9hipWrIgtW7bg8uXLuHr1Ktzd3bFz506N1w2Fh4fD2tq6WEkTAJiYmKB79+7Ytm2bmiIjpOSixInoBE2drNPV03SXLl1Cw4YNMWLECGzevLnYTS3zkp6eji1btmDs2LEamyMvEokEc+bMwdSpU3nf5bpq1ao4evQodu3ahQ0bNqBWrVo4c+aMxuIu7jbdl0aOHIktW7ZovNksIbqOEieiEzRxsk4ul+PUqVM6lTixLIt169ahT58+2LNnD8aOHavxova9e/eidu3acHV11eg8eenXrx/i4+N5vV33pUaNGuHGjRuYNWsWfvzxR7Rs2RKBgYFqn0d11Yo61K5dGxYWFjh79qxaxiOkpKLEiegETRSI3759G3Z2drC1tVXruJoilUoxYsQIbNiwATdv3oSPj4/G59R2C4LcCIVCzJ8/H9OnT9eZY/MMw6BLly4IDQ1Fjx490KFDB/Tp0wfR0dFqm6O4J+q+prq/jhCSO0qciE7QROKkS9t0Xza1vHXrllqbWubl+vXr+Pz5M1q3bq2V+fLSuXNn6OnpYd++fVyHUihisRgjR45EREQEqlatCm9vb4wfPx4fPnwo1rhKpRL3799Xa+LUu3dvXL16Fa9fv1bbmISUNJQ4EZ2gicRJV9oQaLKpZX64aEGQG4ZhsHDhQsycOZPX98blxtjYGDNnzsTjx4+hVCpRpUoVLFiwAKmpqUUaLyoqChYWFihbtqxaY+zTpw+2bt2qtjEJKWm4fzUkpABUxeHqKrKNjo7Gx48fUadOHbWMpymqppZLly7VSFPLvLx8+RIXL17EwIEDtTZnflq0aAFHR0f89ddfXIdSZJaWllizZg1u376NkJAQuLq6YsuWLZDL5YUaR93bdCojRowoUjyElBaUOBGdYG5uDrFYXOztDZWTJ0/Cz8+PFyspOVEqlfjtt98wadIknD17Fj179tR6DBs2bMCAAQO0usJVEAsXLsTcuXORlpbGdSjF4uzsjH379uHIkSPYs2cPqlevjiNHjhT4zUFxr1rJjaenJ+zt7XWmEJ8QbePnTw1CcqDOk3V8rm9KTk5Gt27dcPHiRdy5cwc1a9bUegxpaWn4888/MWbMGK3PnR9vb294e3tj3bp1XIeiFnXr1sXFixfx+++/Y+bMmWjcuDFu3ryZ7/M0teIEgDqJE5IHSpyIzlBXnVNycjJu3bqFVq1aqSEq9VI1tSxXrhwuXrwIKysrTuLYs2cP6tevD2dnZ07mz8+8efOwdOlSJCYmch2KWjAMg3bt2iE4OBjDhg1D79690bVrVzx58iTHx7Msq9HEqWfPnrhz5w6eP3+ukfEJ0WWcJU7JMgWik6R4Ep+BR5/S8SQ+A9FJUqTIdOOoMdE+dSVO586dQ4MGDXi3BaXNppZ54UsLgrxUq1YNvr6++P3337kORa2EQiEGDhyI8PBwNGjQAI0bN8aIESO+6Zr/7NkzGBsba6xbvIGBAfr3748tW7ZoZHxCdJlIWxOlyZUIiUtHVKIUsekKyJUsRAwDFv/t5zNgIGdZiAQMLPWFcDaVwNNCHwYiWhgjmYnT6dOniz0O37bpWJbFhg0bMGfOHOzdu1cr/ZnycvXqVchkMrRs2ZLTOPIzZ84c1K5dG2PHjtXodTNcMDAwwOTJkzFkyBAsWrQIHh4eGD16NCZPngwTExONrjapjBgxAj4+Ppg9ezbEYrFG5yJEl2g8I3mTKsORZ0lYG/oJ195+xqtUOTIULBQskKFkIVUi61eG8v8fV7B4lSrHtbefsTb0E448S8KbVN07fkzUSx3XriiVSpw8eZI3iZNUKsXIkSOxfv16rTW1zM+qVaswbtw4jXckLy5HR0f07dsXCxcu5DoUjSlbtiyWLVuGoKAgvHz5Eq6urlizZg3u3Lmj8cSpSpUqcHV1xdGjRzU6DyG6hmE1dIlSmlyJ0y9T8DRJCgULFGcSBoCQAZxMJGhrb0wrUKVUbGwsqlatio8fPxZ5jICAAAwZMgShoaFqjKxoYmNj0b17d5ibm2PXrl282Dp8/vw5ateujRcvXsDY2JjrcPL1/v17VK1aFffv34e9vT3X4WjcgwcP8Ouvv+LKlSsYN24cFi1apNGToXv37sW2bdtw7tw5jc1BiK7RyHdcZGIGNj6OR1SSFPJiJk1A5vPlLBCVJMXGx/GITMxQR5hEx5QvXx5SqRTx8fFFHoMv23SqppZNmzbF4cOHeZE0AcD69esxaNAgnUiaAMDKygojR47EnDlzuA5FK7y8vODv7w+xWIzTp0+jXr16uHTpksbm69q1Kx48eKCRC7YJ0VVqTZxYlsWF1yk49jw5aztOnVTbeMeeJ+PC6xTe35RO1IthmGK3JOBD4sRlU8u8pKamYtu2bbxsQZCXyZMn49ixY7meQCtpXr16BUNDQ9y/fx8///wzhg4dCl9fX4SEhKh9Lj09PQwaNAibN29W+9iE6Cq1vWKzLItTL1MQHJcOTR+MkymB4Lh0nHpJyVNpU5yTda9fv8arV69Qv359NUdVMEqlErNmzeK0qWVedu/eje+++05r9+Cpi5mZGX7++WfMnDmT61C0QlUYLhAI0Lt3b4SFhaFdu3Zo3bo1Bg4ciJcvX6p1vuHDh2P79u3IyKCVfkIANSZOF2NSEZaQofGkSUWmBMISMnAxpmj3PBHdVJwC8RMnTsDX1xcikdYOk2ZJSUlB9+7dceHCBc6aWuZFF1oQ5OXHH3/EjRs3EBgYyHUoGvf1iTqJRIJx48YhIiIC9vb2qFmzJiZPnlysLe0vOTs7w8vLC4cOHVLLeIToOrUkTpGJGVpZafqaauWJap5Kj+KsOHG1Tffs2TM0bNgQZcuWxYULFzhrapkXVZ0MH071FYWhoSFmzJiBadOmcR2KxuV21YqJiQnmzZuHhw8fIikpCa6urli2bBnS09OLPSd1EifkP8VOnNLkSpx4kaL1pElFpgROvEhBmpwaZ5YGRa1x+vz5M65evYo2bdpoIKrcXbp0CQ0aNMCwYcOwZcsW6OnpaXX+glKtNvG9BUFehg4disjISFy+fJnrUDQqvx5ONjY22LRpE65du4abN2/Czc0Nf//9NxQKRZHn7NSpEyIiIhAWFlbkMQgpKYqdOJ1+mQK5kts6I7mSxelXKZzGQLSjqCtOFy5cQJ06dWBmZqb+oHLAsizWr1+PPn36YPfu3bzui/Ts2TNcv34d/fr14zqUYpFIJJg7dy6mTZtWYmsf37x5A5lMBjs7u3wf6+7ujsOHD2PPnj3YvHkzatasiVOnThXp30YsFuOHH37Apk2bihI2ALotgpQcxerj9CZVhj2RiZDz4DVKxAD9XExhbUQdbksypVIJY2NjxMbGFurI/IgRI+Dm5oaJEydqMLpMUqkU48aNw40bN3D06FE4OTlpfM7imDRpEgQCAZYuXcp1KMWmUChQo0YNLFy4EB06dOA6HLU7ceIE1qxZgzNnzhTqeSzL4ujRo/j1119hY2ODpUuXok6dOoUa4/nz56hTpw5evXoFAwODfB9Pt0WQkqpYX513YtPU3nKgqBQsEBCbxnUYRMMEAgGcnJwKtV3HsqzW6ps+fPiAli1b4t27d7h16xbvk6aUlBRs374do0eP5joUtRAKhViwYAGmT58OpbLkrWQU9aoVhmHQuXNnhIaGonfv3ujUqRN69epVqO8jR0dHeHt7Y//+/Xk+jm6LICVdkROnNLkSkYnSYje3VBcWQGSilGqdSoHCnqwLDg6GkZERXF1dNRhVZldnb29vNGnShFdNLfOyc+dONGnSBI6OjlyHojYdOnSAkZER9u7dy3UoahcYGFisq1ZEIhGGDx+OiIgIVK9eHfXq1cO4ceMQGxtboOePHDky1+26NLkSh6OTsCcyEeEJmTdGFHY3Qs5mvgkOT5BiT2QiDkcn0Ws64Z0iJ04hcenIr2Ljw/MorO7dDLMaOeLG3qI3UDswayzOrsv/Pirm/3GRkq2wBeLHjx/X+LbNv//+i5YtW2Lx4sWYP38+b5pa5kXXWxDkhmEYLFy4EL/99hukUinX4ahVUFBQjifqCsvIyAgzZsxAWFgYBAIBqlatinnz5iE1Ne/2Lu3atcOrV6++abZJt0WQ0qTIr+5RidJ8301c/XsNKtdphDnXn+O7PsOLOlWBydnMuEjJVtgCcU1u06maWv788884c+YMevXqpZF5NOH8+fMQi8Vo2rQp16GoXfPmzeHk5IStW7dyHYraxMbGIjk5GZUqVVLbmOXLl8eqVasQEBCAx48fw8XFBZs2bYJcLs/x8SKRCEOHDs1adaLbIkhpVOTEKTY9/6Ot8W9fw8rJrahTFElB4iK6rTCJ07t37xAZGYlGjRqpPY6vm1pq+rZ6dSsJLQjysnDhQsyfPx+fP3/mOhS1UNU3aeL/l5OTE/bu3Ytjx45h//798PDwwOHDh3NMVIYMGYK9e/ciOTmZbosgpVKREqdkmSLfFgRbhndB9L3rOLZkKmZ954CFrT1w9/DOrM8HHtuLjT/4Zf059lkkto7qjrnNXPB7l/oIOXukKKFBrmTpeGsJV5jEyd/fH61bt4ZYrN7TlrrQ1DIvUVFRuH37Nvr27ct1KBpTp04dNGjQAGvXruU6FLVQ1zZdXurUqYPz58/jjz/+wOzZs9GoUSPcuHEj22NsbW3RpEkTbL3+iG6LIKVSkRKnD2kKiPJ51zNs82E41qyPjlMWYc6NFyjnkPvpImlaKraN7g6vtl0x/XwY+izajKOLp+B9dHihYxMxDGLTcl5mJiWDnZ0dYmNjkZaW/ylKTdQ3Xb58WSeaWuZl3bp1GDp0KAwNDbkORaPmzZuH5cuXIyEhgetQiq2oJ+oKi2EYtG3bFkFBQRgxYgT69u2Lzp07Z2t+2fvHKUgxt6XbIkipVKTESapgs/XiKK4nV8/C3NoOdTr1hVAkgo27Jzx82uPhuWOFHosFCylfeiQQjRAKhXBwcMCzZ8/yfFx6ejouXryItm3bqm3uDRs2oFevXrxvapmX5ORk7NixA6NGjeI6FI2rUqUK2rdvj+XLl3MdSrHldtWKpgiFQgwYMADh4eFo1KgRmjRpguHDh+PZ6zd4W84VIj19rcXyJbotgnCtSImTQs17zPFvX+NVaBDmNHHK+hV86l+kxBXsiOyXWA3ER/inICfrrly5gurVq6NcuXLFnk8qlWLkyJFYu3Ytbt68iRYtWhR7TK7s2LEDPj4+sLe35zoUrZg1axY2bNiA9+/fcx1KkX369AlxcXFwdnbW+tz6+vqYNGkSIiIiYGZmhrkHLyJdym2PJbotgnCpSNfEC4vwLltiYAhp+n9bK8lfJEWmFSqiUu2GGLLh36KEkw1TxPiIbilIndPx48fVcpruw4cP6NatG8zMzHDr1i2YmJgUe0yuKJVKrFmzBlu2bOE6FK1xcHBA//79sWDBAqxevZrrcIrk/v37qFGjBqdtLszNzfHTrAXYHZEARb7NaDRLwQJPE6V4myqj2yKI1hXpu1AiZMAU8hvH2tUDjy6ehDTtMz6+jMa9I7uzPlelcWt8fPEUQSf2QyGTQSGT4dWj+4iNjih0bAqFAp+TEujkRQnn7OyM6Dfvc737StUtvLj1TV82tTxy5IhOJ00AcO7cORgYGGjklCGfTZ8+Hbt378bz58+5DqVItL1Nl5s7sWlQcpw0qdBtEYQrRVpxKm8ghLyQiUmjfiPx+nEwFrSqCmuXqqjh2x1Rd64AAPSMjPHD+gM4uWIm/Ff8BiWrhLVLNfj9PLfQsUnlCnRp1QKJsW/h6uoKV1dXuLm5Zf3XxcUFRkZGhR6XcO/Lu6/SGvZAhXpKHH2WnOPdVwyrRMf5m5FczhFpcmWR7r46ePBg1vacLvVnysuqVatKdAuC3FhaWmL06NGYPXs2tm/fznU4hRYUFAQ/P7/8H6hBfL4tgu62I9pU5Et+V4bEIYOHRdh6QgYTPC0QFxeHiIgIhIeHIyIiIuv3UVFRKFeuXI5JlYODA0SiIuWSRIPepMpwJzYNkYlSMCjcNQ4iJvMF1sVUAm9LA9gUYFlfqVRi7ty5+Ouvv3D48GGd68+Um4iICDRq1AgvX76Evj43hb1cSkxMhIuLCy5fvoyqVatyHU6huLi44MiRI6hWrRpnMQS8/4xrbz9jQbta6PbbSjjXK37j1M3DOqFmu+6o2+X7Ij1fxACNrQ1Rz6pknw4l/FLkLMFSX4hXqfw79m+pLwQAWFhYoEGDBmjQoEG2zyuVSrx8+TJbUnXq1CmEh4fj3bt3qFSpUrZkSvXf8uXLl7p36VxLkytx+mUKniZl3ntVlDRdlWSFJ0gRlSiFk4kEbe2Nc32HmpKSggEDBuD9+/e4c+eOzvVnysvatWsxbNiwUpk0AYCpqSkmT56MmTNn4uDBg1yHU2CJiYl4+/Yt3N3dOY2jILdFaJvqtghKnIg2FTlxcjaV4O1nOa++kURMZlx5EQgEcHR0hKOjI1q3bp3tc2lpaYiKispaobp27Rq2bt2K8PBwKBSKb5IpV1dX2vrTkMjEDJx4kQK5Uj3XOHx991V7B2O4mGbvv/Ts2TN06tQJ3t7e2Lt3r072Z8pNUlISdu3a9c0dY6XN2LFj8ccff+Du3buoW7cu1+EUSHBwMDw9PSEUCjmNQ523MrAsq7Y6VLotgmhbkRMnTwt9XH3Lr6sMWGTGVVQGBgaoXr06qlev/s3nvt7627dvHyIiIrJt/X2dVNHWX+GxLIuLMakau8ZBwQKK/999VcNCBp+KRmAYBpcvX0bv3r0xffp0jB07tsStLm7fvh2tWrWCra0t16FwysDAADNnzsS0adNw7tw5rsMpkMDAQM63i7++LeL1o/s4vnQakj6+R7Vmvug0bRnkGenYN2M0XocGQaGQw8HLG12mL4eplQ2AzG05By9vPAu8gZgnDzF+35Vv5rl3ZDeu7liH5LhY2FWriS4zVsDcxg5HF/0CkZ4+/Cb+V/e646f+qFynEZp+PxIpMiWMxVTnRLSjyDVOAHDkWRLCE/hRLMgAcDOToHMl7Z56UigUePXqVbakSvXf9+/fo1KlStmSKdr6yx3Lsjj1MkVr1ziIBUAVMz0899+FObNnY8+ePTrdnyk3SqUSbm5u2L59O7777juuw+GcTCZDlSpVsHnzZvj4+HAdTr769++PFi1aYPDgwZzFEJ0kxdFnychQsljiVwt6hkYYtOYfSAwMseOnfqhcpxG+6zcSzwJvwLVhC7BKJf6d8yOUcjm+X7EDQGbi9CnmBQav+QflHJwBlsXW0d2zapweXz6Fkyt+w8A/dsPCvjKubF+F8OsXMGq7P16FBmHnxAH49XQIBAIBUuPjsMSvJiYfv4dy5a3QqVIZVDbJe7eBEHUp1nKIt6UBb/a9hQxQz9JA+/MKhQXa+gsPD/9m6+/rZKq0b/1djEnV+t1Xwe+T8SROiZs3b8LJKfdrgXTZ6dOnYWpqioYNG3IdCi+IxWLMnTsXU6dOxe3bt3n/BiYoKAiTJ0/mNIavb4to0GsIzCpUBAA0GzIBx5dOQ+sx0+DR4r/2H82HTMCW4V2yjVO7Q29YOeVcqxXw73Y0+2E8LCu7Zo77wwRc3rYK8W9ewc6jFvSNTfD0zlW41G+GB2cOo1LthihjYUm3RRCtK1biZGMkhpOJBFH/L97lipABnEwlvGuElt/W35cn/kr71l9kYoZWbln/mkAsgWf7XlCWK6PdibVo9erVpbIFQV569+6NJUuW4OjRo+jcuTPX4eQqJSUFL1684PwU4Ne3Mai23wDA3NoOSR/eQZr2GSd/n4mImxeRlpwAAMhITYFSoYDg//VZplYVc50j4e1rnFg2Hf4rZmV9jGVZJH14C3MbO9Tq0Av3/Q/ApX4zBPv/i4Z9h2c+Jof4CNGkYv8UbmtvjI2P46HgMHMSCRj42hlzNn9RWFhYoGHDht+sAqi2/r5Mqvz9/XPd+lP9V5e3/tLkSpx4kaL1pElFzmbefTWyqrjE9YN58uQJgoODcfToUa5D4RWBQIAFCxbg119/RYcOHTgvvM7NgwcPUK1aNYjF3L4p/Po2hsT3b7J+n/DuNUzKV8C1XRvw4UUURu84jTLlrPAm/CHW9PHJVgSe10uUqZUNmg2ZgJrtuuf4+ZrteuCPHo3xNiIUsc8jUK2Zb+aYOcRHiCYVO3EyEAnQ3sEYx54nc/KDTywA2jsYQ7+E/MD7cuuvTZs22T6X09bfn3/+ifDwcLAs+822n65s/Z1+mZKt8JQLqruvumi5Rk7T1q5di+HDh5eoE4Lq4ufnh0WLFmHPnj34/vui9RHStKCgIM4Lw4Evb4vI/D69tX8b3Bu3hljfAJe2roRnq06QpqZArKcP/TKm+JwYjwubC3excr3ug3Buw2LYuHnAyskd6clJiLx9CdVbdQKQmVjZVquJ/TPGwMOnPcT6maUZDBhIhJQ4Ee1Ry76Pi6kealjItL7VIhYANSz0vzlWXlLltfX38ePHbI0+9+3bh/DwcDx9+hTlypX7ZoXK1dUVjo6OnL/TfpMqy+rTxKWSePdVYmIi9uzZg9DQUK5D4SWGYbBo0SIMGjQIvXr1gkTCv+LiwMBAXhT0f31bRI22XbFtdA8kfXiHKs3aovnQiUhPScI/00Zgvo8bypSvgMb9R+PxJf8Cz1HNxw8Zn1Oxd+pwJLx9BX1jEzjXa5qVOAFArfa9sH/maLSfvCDrY3KWhaVBySxhIPxUrFN1X+LiRFRVMz20tTfW2S0qbfh66+/LLUDV1l9ORera2vqjk5mas3LlSty9exd79uzhOhRea9u2LTp06IAxY8ZwHco3PD098ddff/Hinjo+3BbxLPAm9s0cjSkn72e9PqluiyBEW9SWOAGa78GjolppUvXgIUWj2vr7OqnKbevPzc0Nzs7Oatv6S5MrsTb0E+erTV8SMsBYj7I6X+ukati6e/du1K9fn+tweC0oKAjt27dHZGQkr7a109LSYGFhgfj4eF5ste6OSOD0tgiFTIa9U4fB2tUDLYZPyvq4nZEI/VzNOIuLlD5qXd9kGAYtbI1hX0as1q7PKkImsxA8p67PpPAKsvX3ZcPPvLb+VHf9FWbrLyQuXeP3rJ/fuBRxr56h14INBXo8g8y4dP0KB39/f1hYWKBevXpch8J7tWrVQqNGjbB69WpMnTqV63CyhISEwN3dnRdJE8DtbRGx0RFY278VrF2rofvsEVkfL8htEYSom0Y2hl1M9TCyqrjY94ypZJ6ayGw50NYu93vGiPqUK1cO5cqVy/HUn+quP1VidfLkSYSHh+P9+/eoXLlyjvVUOW398aUH2JdKyt1X1IKgcObNm4dGjRph5MiRMDc35zocAPwpDFfh8rYIy8qumHvzxTcfL+5tEYQUhcYq6gxEAnSpbIK3qTIEqOFm+3qWBiWmaFeXCYVCVKpUCZUqVcr11J9qlerKlSvYsmVLtq2/L5Oqt85NARQ8CVbI5RBqoY+Vrt999fjxY4SGhqJHjx5ch6Iz3Nzc0KlTJyxbtgwLFy7kOhwA/Lhq5UsGIgFcTCW8qkl0MZXQG2midWqtccpLmlyJkLh0RCVKEZueee+RiGGg6kfLIPNYqZxlIRIwsNQXwtlUAk8LffrG0HEsy35z1190zDtUHzMfIknmNkTCuxicWDYdz+/fhpJVwqtNF9hWrYG7h3fCtlotBJ3cj/rdB0EgFGXbeot/8xJL29fG/DtvIRSJ8CnmBf6dNQ4xT0JgX70Oyjk4IT05KevxL0Pu4eSK3xAbHQ4zazt0mLwAletkP7UkZIBR1crq7N1Xo0aNQoUKFTBr1qz8H0yyvHr1CjVq1MCjR49QoUIFrsNBrVq1sGHDBl5tt75JlWFPZCIvVopFDNDPxZTeUBOt09oZTgORAPWsDLO2QFJkSsSmySFVsFCwLIRMZi8OSwORzv7AIjljGOabrb8v775SKhT4e3w/ONVthF/mB4IRCBHzOBhxr57hVWgQPNt0wfRzj6GUy3Bl+5o85/pn2kjYe9bBD+sP4NXDQGwf3xdVm2Y2ykuMfYvt4/ui57x1cG3YAk/vXMWuyYMx8dBNGJuXyxpDxDCITZPDWKx7tRPx8fH4559/EBYWxnUoOsfOzg4DBw7E/PnzsXbtWk5jycjIwJMnT+Dp6clpHF+j2yIIKcw+iZoZiwWobCKBu7keqpXVh7u5HiqbSChpKiW+vPvqVWgQkj+8g+9PsyExMIJYTx+ONTNPgpUpXwENew+DUCTKaniXm4S3rxHz+D5aj/4VIokeKtVuiCpN/ttODPY/ALfvWsK9USsIBAK41G8G2ypeCL9+Pts4unz31bZt29C+fXterJjooqlTp2Lv3r149uwZp3GEhobC2dkZBgbav38zP23tjSEScFs7p4u3RZCSg7qGEU58ebdU4vsYmFnb5li/ZPbFnVj5SfrwDgZlzCAx+O9IuZm1LRLfZV4PEf/2NULPH8OTq2f+i0MuR+W6jbKNo6t3XykUCqxduxb79+/nOhSdVb58eYwdOxazZs3Cjh07OIuDb4XhX6LbIkhpR4kT4cSXd0uZWlVEwruYnIu/vzoVJjEwhCw9LevPyR9js35fppwV0pITIE1LzUqeEt7F/P+qiMwkrKZfD3SduTLP2HT17qsTJ06gQoUKqFu3Lteh6LSff/4ZLi4uCA0NhYeHBycx8DlxAui2CFK6UcpOOPHf3VeAnUctlClnhTNr5kGalgpZRjqeBwfk+DxrNw88u38LCW9fIz05CZf/WpX1OXMbO1SsUgPnNy6FXCbF8/u3s60u1WjXA2FXzyDi5kUoFQrIMtIRfe9GtgtLAd29+2rVqlX48ccfuQ5D55mYmOCXX37BjBkzOIshMDCQF93C8+JT0QhVzPSgreoK1W0RPhX506SUlE6UOBFOfHn3lUAoxMA/diHu1TMsblcTi329EHL2SI7Pc6nfDJ6tOmFVr6ZY068F3Bu3zvb53gs34tXDQMxr5oILm5ejpl/PrM+ZVaiI71fsxOVtf2B+C3cs8a2BqzvWglVmf8usi3dfPXz4EE+ePEG3bt24DqVEGD16NAIDAxEQkHMCr0kymQyhoaHw8vLS+tyFwTAMfO2NUcNCX+PJk2qlia7YInygtXYEhHyND3df5UQX774aPnw47OzsMHPmTK5DKTG2bNmCf/75BxcuXNDqvA8ePMD/2rvPgCiuLYDj/4GlCYIVexc1VsBYYsUuFhRbLDE2Yk9Ro1ETe9TE2I1GjV1jixUNWMCuUVCx915QsSDS2TLvAw8SY6Ps7uwu9/flPd3duYcI7Nl7z5zTuXNns7oz8vCVuwRHaMiW3RmdHucBiGkRgikSO06CYlzt0z6exZhMNa53ef78OX/++Sd9+/ZVOhSL0rNnT+7fv09QUNCHn6xHp0+fNvljun+TZZlxA3vhFBaAm4sdKolMp04S/4xT6V8+p0iaBJNiXucRgkVRcvbVu5jj7KulS5fi4+NDvnz5lA7FotjY2DBx4kRGjx5No0aNjHZEZOqF4f+1dOlSoqOj+WbQAFQqlZgWIVg8cVQnKCZeo+PXCy8UbaT3X9YSDK6Yy2y61Ws0GkqVKsWWLVvMapfCXOh0OqpWrcqYMWNo166dUdasVasWU6ZMwcvLyyjrZUZ4eDju7u4EBQW90azzv9Mi4hISsbGSUNnYiGkRglkTiZOgqG23X5nU7KuyOWxpW8JZ6VDSbMuWLcycOZMjR44oHYrFCggI4Ntvv+X8+fNYWxv2GFer1eLi4sLDhw9xcXEx6FqZJcsy7dq1o2LFikyaNOmDz1+9cTM7Dhxl4uSpYlqEYNbEd6ugqOquDpjKnf/WEtRwNb1Oze8zd+5c0YLAwLy9vcmdOzerV682+FpXr16lQIECJp80AWzevJkrV66kuW1DpzatOLBpDdbP7olpEYJZE9+xgqJSZl8pnTyZ4+yrs2fPcuPGDXx9fZUOxaJJksTUqVMZP348iYmJBl3LHPo3Abx48YKvvvqKpUuXYmeXtsJtOzs7evbsyeLFiw0cnSAYlkicBMWJ2VcZM2/ePAYOHIiNjfkke+aqTp06VKhQweBv+uZSGD5s2DDat2+fOrQ7rfr27cvKlStJSEgwUGSCYHgicRIUlzL7Sqkde3OcffXs2TM2b97MF198oXQoWcbkyZOZMmUKMTExBlvDHBKnvXv3sn//fqZMmZLu15YuXZoqVaqwZcsWA0QmCMZhPu8UgkVLnn1lj8rIG0/mOvvq999/x9fXl7x58yodSpbh7u5O/fr1mTNnzoefnAE6nY6wsDCTTpxiYmLo27cvCxcuJHv27Bm6Rv/+/Vm4cKGeIxME4xF31QkmIyEhgW+Xb6dg1bpY2Ri+l5I6IQ6Hl48Y1ry6WY1xUKvVlCxZEn9/fzw8PJQOJ0u5fv06n3zyCdeuXSNXrlx6vfa1a9do1qwZt2/f1ut19WnIkCE8f/6cVatWZfgaarWaokWLEhwcTPny5fUYnSAYh9hxEkyCTqejR48ePA7+k+oFshtl9lVFFxXz+ndi6dKlhl1Mz7Zt20aJEiVE0qQANzc32rVrx88//6z3a586dcqkd5uOHz/O+vXrmTVrVqauY2NjQ58+fVi0aJGeIhME4xKJk6A4WZYZNmwYjx8/Zs2aNTQukh2f4tmxs5b0fredtZQ8i86neHZ8yxckKCiI8ePHs2bNGv0uZECiBYGyxo4dy++//054eLher2vKo1aSkpLw8/Nj9uzZ5M6d+TmOX3zxBWvWrCEuLk4P0QmCcYnESVDcjBkz2Lt3L9u2bcPe3h5IrnnqXz4npZ1tDTr7ys3NjT179jB8+HA2bdqUyVUM7/Tp09y9e5e2bdsqHUqWVbhwYXr37s2PP/6o1+uacmH41KlTKVmyJJ06ddLL9YoVK0bNmjXZuHGjXq4nCMYkapwERf3xxx+MGjWKY8eOUbhw4bc+xxizr86cOUOzZs1YunQprVq1Sv8XYiS9evWiXLlyfPfdd0qHkqU9e/aMcuXKceLECUqVKpXp68myTK5cubh69Squrq56iFB/Ll68iJeXF2FhYe/8Gc0If39/pk6dyt9//623awqCMYjESVBMUFAQ3bp1Y9++fVSoUOGDz0+ZffXngRPkK10erK1RSRIysl5mX4WGhtKyZUv++OMPmjRpkvkvUM8iIiIoW7YsN27c0MtxiZA5EydO5Nq1a3o55r116xb169fn/v37eohMf7RaLbVr16ZXr17069dPr9fWaDSUKFGCnTt3UqVKFb1eWxAMSSROgiLCwsJo1qwZmzdvpm7duml+nVqtJkeOHDx+/BjJ3pGIeA1JWllvs6+OHDmCr68vmzZton79+hm6hqFMnjyZO3fu8PvvvysdigBER0dTunRpgoKCqFSpUqautWnTJlavXs327dv1FJ1+zJ49m61bt7J//36srPRf2TFhwgSePHnCggUL9H5tQTAUkTgJRnf79m3q1KnDvHnz0j1xPiwsjM8++4yLFy8aKDoIDg6mS5cubN++nU8++cRg66SHWq2mRIkSBAQEvDGFXlDOrFmz2L9/P/7+/pm6zqhRo7C3t2fcuHF6iizzbt++TbVq1Th27BhlypQxyBoPHz6kUqVK3Lt3Dycn8+rcL2RdojhcMKqnT5/SvHlzRo8ene6kCSAkJITq1asbILJ/NGrUiJUrV9K2bVtOnz5t0LXSasuWLbi5uYmkycQMGDCAM2fOcOzYsUxdx9TuqJNlmb59+zJ8+HCDJU0AhQoVol69eqxbt85gawiCvonESTCa2NhYWrVqRYcOHRg0aFCGrmGMxAnA29ubhQsX0qJFCy5cuGDw9T5kzpw5ogWBCUrZJRo9ejQZ3byXZdnk7qhbuXIlL168YNiwYQZfS3QSF8yNSJwEo9BoNHz66aeUL18+U7dxh4SEUK1aNT1G9m6+vr7Mnj2bpk2bcvXqVaOs+TahoaGEh4fTunVrxWIQ3q1Hjx48fvyYvXv3Zuj19+/fR6VSUbBgQT1HljGPHz9mxIgRLF26FJVKZfD1mjZtyosXLzh58qTB1xIEfRCJk2BwsizTr18/dDodixcvzvB4k5iYGG7dumXU46rOnTszZcoUGjduzK1bt4y27r/NmzePQYMGGeVNTEg/lUrFpEmTMrzrZGq7TV9++SV9+vTB3d3dKOtZWVmlzr8TBHMgEifB4MaNG8e5c+fYuHEjNjZv76OUFqdPn6ZSpUrY2hp+jt2/9ezZk9GjR9OoUSPu3btn1LUfP37Mjh076NOnj1HXFdKnffv2yLLM5s2b3/mcaLWWW6+SuBKZyMUXCVyJTOTWqyROX7xiMonT1q1bOXfuHGPHjjXqur169WLz5s1ERUUZdV1ByAjxEVYwqEWLFrFu3TqOHj2a6btmjFXf9DYDBgwgISGBRo0acfDgQaMdqyxevJhOnTrpfaCsoF9WVlZMnjyZb775hrZt26JSqVL7jt2ISiIiQYtGJ6f2HUshIWHX5HNsrK3449rLdPUd07eXL18yePBg1q1bh4ODg1HXzp8/P02aNGHNmjUZrn8UBGMR7QgEg9m2bRsDBw7k8OHDeumu/Omnn9KqVSu6d++uh+gyZsqUKaxZs4YDBw4YvMNzUlISxYsXZ8+ePVSsWNGgawmZJ8syXl5edB3wNflqNM50p/vqrg4UfEene0P44osvsLGxUaynUnBwMEOGDOHs2bMZPs4XBGMQO06CQRw9epS+ffsSEBCgl6QJknecJk6cqJdrZdTo0aOJj4+nadOm7Nu3z6A7QZs2baJ8+fIiaTITCVqZz6av4LFGxcuXSWTkE2lKknX1ZRI3opIo5WxL86JOBt+B2rdvH7t371b0DtIGDRqQkJDA33//Ta1atRSLQxA+RNQ4CXp36dIl2rVrx+rVq/n444/1cs2IiAgiIyNxc3PTy/UyY+LEiTRu3JjmzZsbtCZj7ty5ogWBmbgelcjCS5G8tMmOjb1DhpKmf5NJTqJuvEpi4aVIrkcl6iPMt4qLi6Nv37789ttvODs7G2ydD0kpEl+0aJFiMQhCWojESdCrhw8f0qJFC6ZPn06zZs30dt3Q0FA+/vhjg4x9SC9Jkvjll1+oVq0aLVu2JCYmRu9rnDhxgoiICFq2bKn3awv6I8sywQ9i8L8TTaJWRqvnwgetDIlaGf870QQ/iMlwr6j3GTduHNWrVzeJ77WePXuyfft2Xrx4oXQogvBOyr8LCRbj5cuXeHt7M2DAAL3XIYWGhipWGP42kiQxb948ypYti4+PD/Hx8Xq9/rx58xg8eDDW1tZ6va6gP7IsE3gvhjPPE1DrDLuWWgdnnicQeE+/yVNoaCirVq1izpw5ertmZuTJk4eWLVuyatUqpUMRhHcSiZOgF4mJifj6+uLl5cWIESP0fn0l76h7FysrKxYvXkyBAgVo164diYn6OU559OgRAQEB9O7dWy/XEwxj38NYLr9MNHjSlEKtg8svE9n3MFYv10tKSqJPnz7MnDmTvHnz6uWa+pDSSVzctySYKpE4CZmm0+n4/PPPyZMnD7NmzdL7HTGyLJtk4gRgbW3NypUryZYtG507d0atVmf6mgsXLqRz587kyJEj8wEKBnE9KtEoO03/lbLzpI+ap2nTplG4cGG6du2qh8j0p06dOlhZWXHo0CGlQxGEtxKJk5ApsiwzdOhQnjx5wurVqw1ytHT79m3s7OxMZiTFf6lUKtatW4darebzzz9Hq9Vm+FqJiYksWrSIwYMH6zFCQZ/iNTp23o0xetKUQq2DnXdjiNdkPIDLly8zZ84cFi5caHK3/kuSJObXCSZNJE5CpkyfPp3g4GC2bduGvb29QdYwtfqmt7G1tWXTpk08e/aMPn36oNNl7E1t48aNVK5cmfLly+s5QkFfdt2LQaNT9hhJo5PZdT9jNyXodDr8/PwYP348RYsW1XNk+tG9e3cCAwOJiIhQOhRBeINInIQMW7NmDb/++iuBgYEGPVYy1WO6/7K3t2fbtm3cunWLQYMGpbtGQ5Zl5syZI1oQmLDwWDU3XyXp/e659NLKcDMqiUex6T8aXrBgAZIkMWDAAANEph85c+bE19eXFStWKB2KILxBJE5ChuzZs4dhw4YRGBhI4cKFDbqWuSROAI6OjuzcuZOwsDCGDh2aruTp+PHjvHz5khYtWhgwQiEzQiLiFU+aUmhlOBGRvrs57969y/jx41myZIlJtPZ4n379+rF48eIM794KgqGY9k+OYJJOnz7NZ599xubNmw1+pKTRaAgLC6Nq1aoGXUefnJ2dCQwM5MCBA/zwww9pft3cuXP58ssvTf4NLauK1+i4HpWxjuCGIAPXo5LSXOskyzL9+/dnyJAhlCtXzrDB6UGNGjVwcnIiODhY6VAE4TXiN7SQLrdu3aJ169YsWrSIOnXqGHy9S5cuUbhwYbO7wyxnzpzs3buX7du38+OPP37w+Q8fPmT37t307NnT8MEJGXLueQKmVUYNEslxpcUff/xBeHi4QdqFGIIkSfTr1090EhdMjkichDR7+vQpzZs354cffsDX19coa5rTMd1/5cmTh6CgIFatWsWMGTPe+9yFCxfSrVs3XFxcjBSdkF43opLSNbA3vSLD7zHKMy9ajSbNr9HIyXF9SEREBMOGDWPp0qXY2BhvcHBmdevWjeDgYB49eqR0KIKQSiROQprExsbSsmVLOnXqZNSi0pCQEKpVq2a09fQtf/78BAcHM3/+fObPn//W5yQkJLB48WLRgsDERSRkvM2EIaUlrq+//poePXrobXaksTg7O9OxY0eWLVumdCiCkEokTsIHqdVqOnXqRMWKFZk0aZJR1zaHVgQfUqRIEYKDg/n5559ZunTpG49v2LABT09PypYtq0B0QlpEq7XpakEQcesai79ow4R6pZjVoQ6XDu4C4MrhPczt0oDxdUvwk3cVghZOS33NIj8fACbWL8W42sW4ezY0TWtpdDIx72kqtWPHDkJDQxk/fnya4zclKUXimemPJgj6pFI6AMG0ybJMv379kGWZRYsWGbVZXlxcHFevXqVKlSpGW9NQSpQoQVBQEA0aNMDe3p5u3boB/7QgmDx5ssIRCu/zNF6LSpLQpuEuSa1azcpvuvFxm670XvAnd8NOsGpodwavCcLWwZFOE+fjWqocT25cZtnAjhQoW5EKDVrQb4k/01pVZezBm1ir0v6rWSVJRMRrcLKxfeOxqKgoBg4cyKpVq8iWLVu6vmZTUbVqVVxdXdm9e7e441QwCWLHSXivMWPGcOHCBf7880+j10aEhYVRoUIFgzXWNLYyZcqwZ88evv32WzZv3gzA0aNHiYmJoVmzZgpHJ7xPklZGTuP9dPfOnyQpPpb6vb5GZWNLqep1KVe3KWd3baHkx7XJ71YeKysrCpSpQOVmvtw+dSxTscnIJL2jR8LIkSPx9vamQYMGmVpDaaKTuGBKxI6T8E6//fYbGzdu5OjRozg6Ohp9fXOvb3qbChUqEBgYSLNmzbC3t2flypWiBYEZSMtOU4rop49xyVfotX/TnAUK8+rpI+6dP8XueZN4cuMKGk0S2qQkKjb2yVRs8jviO3jwIDt27ODChQuZur4p6Ny5M8OHD+f+/fsUKVJE6XCELE78thbeauvWrfz444/s2rVLscnpllDf9Dbu7u7s2LGDzz//nF27dtGjRw+lQxI+wDodR9TZ8+Yn6snD1xo3vnz8EOe8BdjwfX8+qtec7wLPMP7QLaq37wGpSU/GjsGlt8QXHx/PF198wfz5882ulcfbODo60rVrV5YsWaJ0KIIgEifhTUeOHKFfv37s2LGDkiVLKhaHObci+JDq1avTokUL1Go1Z86cUToc4QNsrSWkNCY2RSpVxcY+G4dWzkOrVnPr5FEuH9pN5Wa+JMbG4OCSAxs7e+5fOM3ZXVtSX+eYMzeSlRUvHt5NV2wSErbWr8c2YcIE3N3dadOmTbquZcr69evH0qVL0aSjXYMgGIJInITXXLp0ifbt2/PHH3/g6empWBzPnz8nIiLCYu80i4+PZ/fu3SxcuJAOHTpw/PhxpUMS3iOvgzWaNB7XqWxs6TF7DdeOBjOpUVm2Tx2RXBBewo02o34m6LefGVenOMGLp1OpyT/HdLYO2WjQZwiLerVkQr1S3Dt3Mk3raWQZV4d/qi5Onz7N8uXLmTdvXvq+SBNXqVIlihUrxs6dO5UORcjiJDm9k0gFi/XgwQNq167N5MmT+eyzzxSNZdeuXfz888/s379f0TgMZdmyZWzZsoWdO3cSEBBAr169CAwMVDRZFd5v1rnnJJrKoLp/sbOWGFI5N5DcOqR69ep88803FnkEvGrVKtatW0dgYKDSoQhZmNhxEgB4+fIl3t7eDBo0SPGkCSy3vgmSWxDMnTuXr776CoAWLVqwcOFCWrZsaRGFvJbK1d5a6RDe6t9xzZgxA1dXVz7//HMFIzKcjh07Ehoayu3bt5UORcjCROIkkJCQQNu2bWnYsCHDhw9XOhzAsuubDh8+TGJiIk2aNEn9O19fX2bOnEmzZs24du2agtEJ71LaxRaViQ2rU0nJcQFcu3aN6dOnG73fmjE5ODjQvXt3fv/9d6VDEbIwcVSXxWm1Wjp37oyVlRXr1q0zidviZVkmf/78nDx50iJvPe7QoQMNGzZk4MCBbzy2fPlyxo0bx4EDBxQtzBfeFK/R8euFF5jSaZ21BIMr5sLOCry8vGjfvj1ff/210mEZ1JUrV/Dy8uLevXvY2r7Z9FMQDE35d0lBMbIsM2TIEJ49e8aqVatMImkCuHfvHpIkUbhwYaVD0bt79+6xf//+dx6l9OrVi5EjR9KoUSPu379v5OiE93FQWeHmYpvBpgH6JwFuLrY4qKxYvHgxarU6S8w7LFeuHOXKlWP79u1KhyJkUabxTikoYtq0aRw4cIBt27ZhZ2endDipUuqbLPG4Yf78+fTo0QMnJ6d3PmfgwIF8+eWXNGrUSEyFNzHVXR2wNpFvS2sJarg68ODBA8aMGcOSJUuwtjbNOix9E53EBSWJxCmLWr16Nb/99huBgYG4uLgoHc5rLLW+KS4ujqVLlzJo0KAPPnfo0KH06NGDxo0b8/TpUyNEJ6RFQUcbSjnbKp48WUtQysWW/NlUDBgwgC+//JIKFSooG5QR+fr6cuHCBVEPKChCJE5Z0O7du/n2228JDAykUKFCSofzBksctQLwxx9/UKtWLUqVKpWm53///ff4+vrStGlTIiMjDRydkFbNizqhslI2c1JZSXgXcWLDhg3cuXOHkSNHKhqPsdnZ2dGzZ08WL16sdChCFiSKw7OYU6dO4e3tzdatW6ldu7bS4bxBq9WSM2dO7ty5Q65cuZQOR29kWaZy5crMmjWLxo0bp+t1w4YN4+jRo+zduxdnZ2cDRimk1fWoRPzvRKPWffi5+mZjBT7Fs5NTHU3FihXZvn07NWrUMH4gCrtx4waffPIJ9+/ft5hB4IJ5EDtOWcjNmzdp3bo1ixcvNsmkCZLvmMmXL59FJU0ABw4cQKfT0ahRo3S9TpIkZsyYQdWqVWnZsiWxsbEGilBIDzcXO9xz22Nj5N+gNlbgntseNxc7hgwZQteuXbNk0gRQunRpPDw82Lx5s9KhCFmMSJyyiIiICJo3b87YsWNp27at0uG8k6XWN6U0vMxIwbskSfz666+4ubnh4+NDfHy8ASIU0qthIUc+ymFntOTJxgrK57CjYSFHAgMDOXr0KJMmTTLO4iaqX79+LFq0SOkwhCxGJE5ZQExMDC1btqRLly70799f6XDeyxLrm27fvs3hw4cz1ZHdysqK33//nXz58tG+fXsSExP1GKGQEZIk4V3UySg7Tyk7Tc2LOhETE0P//v1ZvHgxjo6Ohl3YxPn4+HDjxg0uXryodChCFiISJwunVqvp2LEjVapUYcKECUqH80GWOGplwYIF9OrVK9NvctbW1qxcuRJ7e3u6dOmCWq3WU4RCRkmSRKPCTvgUz46dtaT3u+2speRZdD7Fs9OosBOSJDFq1CgaNWqUrlo5S2VjY0Pv3r1FkbhgVKI43ILJskyvXr149uwZ27ZtQ6VSffhFCkpISCBXrlw8f/4cBwcHpcPRi9jYWIoVK8bJkycpXry4Xq6ZlJSEr68vzs7OrFmzJsv07jF18Rodu+7FcPNVEloZMvOLVeKflgPNizjhoEr+jHv06FE6derEhQsXyJkzp17iNnd3797F09OT+/fvky1bNqXDEbIAseNkwX744QcuX77Mhg0bTD5pAjhz5gzlypWzmKQJYM2aNdStW1dvSROAra0tmzZtIiIiAj8/P3Q6BW7tEt7goLLCt6Qz3dxcKJsjuddTemfbqaTkhKlsDlu6ubngW8I5NWlKSEjAz8+PuXPniqTpX4oVK0bNmjXZuHGj0qEIWYRInCzUggUL+PPPP9m5c6fZ1EFYWn2TLMupReH65uDggL+/Pzdu3GDw4MGIjWPTUcDRhrYlnBlcMRd1C2SjiKMq9RjPzkrC1iq5ZsnWKvnPKcdxRRxV1C2QjcEVc9G2hDMFHG1eu+6PP/7IRx99RPv27RX6ykyX6CQuGJM4qrNAW7Zs4csvv+TIkSOUKFFC6XDSrHv37nh5edGnTx+lQ9GLoKAghgwZwrlz5ww2PubVq1c0adKE2rVrM2PGDIscU2MpYtQ6IuI1JGlltLKMtSRhay3h6qDC6QPV5WfPnqVJkyacPXuWAgUKGCli86HRaChRogQ7duzA3d1d6XAECyd2nCzM4cOH6d+/Pzt37jSrpAksrxVBZloQpJWzszO7du1i//79jBkzxmDrCJnnZGNFSWdbyuW0o0Iue8rltKOks+0HkyaNRkOfPn2YOnWqSJreQaVS8cUXX4jWBIJRiB0nC3Lx4kUaNmzImjVraNKkidLhpMvLly8pXLgwL1++NIt6rA+5efMmNWvW5O7du0YpWH369CleXl507dqV77//3uDrCcYzffp0AgMDCQoKEjuK7/Hw4UMqVarE3bt3yZ49u9LhCBbM/N+hBADu37+Pt7c3s2bNMrukCeDkyZN4enqafNIUrdbyNF77weOW+fPn07t3b6Pd5ZM3b16CgoKoX78+Dg4ODB061CjrCoZ148YNfvrpJ0JCQkTS9AGFChWifv36rFu3jr59+yodjmDBTPtdSkiTyMhIvL29+eqrr+jatavS4WSIqR7TxWt0nHuewI2oJCIStGh0MipJQv7XzeYSEhpZRmUl4WpvTRF7mQ1bt3PswD6jxlqgQAGCg4OpV68e9vb2DBw40KjrC/olyzJ9+/Zl9OjRlCxZUulwzEK/fv0YPXo0X3zxhUg0BYMRR3VmLiEhgWbNmuHp6cnMmTPN9pdF27Zt6dq1K506dVI6FADCY9WERMRzPSoJCdCk56dEq0Gn01E+rxPVXR0o+J+7owzt9u3b1K9fnwkTJtCrVy+jri3oz5IlS1i8eDF///236NWVRjqdjlKlSrFx40aLukNXMC0icTJjWq2WTz/9FJVKxdq1a7GyMt9a/4IFC3L06FHFC9oN0sTQ2ZbmRf9pYmgM165do0GDBkyfPp0uXboYbV1BP8LDw6lSpQrBwcFUrlxZ6XDMytSpU7l58yZLlixROhTBQonEyUzJssxXX33FxYsXCQwMxM7OTumQMuzhw4e4u7sTERGh6I7Z9ahEdt6NQaOT0erxp8JaApWVRKtiTri5GO/f6cKFCzRu3JgFCxbQrl07o60rZI4sy/j6+lK5cmUmTpyodDhm58mTJ5QrV47bt2+TI0cOpcMRLJD5blFkcT///DOHDh1i69atZp00wT/1TUolTbIsE/wgBv870SRq9Zs0AWhlSNTK+N+JJvhBjNGaVVasWJGAgAAGDBhAQECAUdYUMm/Tpk1cu3ZN3B2ZQfny5aNJkyasWbNG6VAECyUSJzO0cuVKFi5cSGBgIC4uLkqHk2lKFobLskzgvRjOPE9AbeDJJWodnHmeQOA94yVPnp6ebN++nZ49exIUFGSUNYWMe/HiBV9//TVLliwx+w9ESurfvz+LFi0SHfUFgxCJk5nZtWsX3333HYGBgRQsWFDpcPRCyVEr+x7GcvllosGTphRqHVx+mci+h7HGWRCoWbMmmzZtokuXLhw+fNho6wrpN3ToUDp06ECtWrWUDsWsNWjQgMTERI4dO6Z0KIIFEomTGQkNDaV79+5s2bKFjz76SOlw9EKn03Hq1ClFEqfrUYlG2Wn6r5Sdp+tRiUZbs169eqxbt4727dtz4sQJo60rpN2ePXs4cOAAU6ZMUToUsydJEv369ROdxAWDEImTmbhx4wZt2rRh6dKlFvVp9Nq1a+TKlYu8efMadd14jY6dd2OMnjSlUOtg590Y4jXGC6Bx48YsX74cHx8fwsLCjLau8GExMTGpb/ROTk5Kh2MRevTogb+/P8+fP1c6FMHCiMTJDERERNC8eXPGjx+Pj4+P0uHolVL1TbvuJd89pySNTmbX/RijrtmyZUsWLFhAixYtuHjxolHXFt7thx9+oG7dujRr1kzpUCxGnjx5aNWqFatWrVI6FMHCiMTJxMXExNCyZUu6detmkWMElKhvCo9Vp/ZpUpJWhptRSTyKVRt13fbt2zNjxgyaNm3KtWvXjLq28Kbjx4+zYcMGZs2apXQoFidlF08UiQv6JBInE6ZWq+nQoQPu7u6MHz9e6XAMIjQ01Og7TiER8YonTSm0MpyIiDf6ul27dmXSpEk0btyY27dvG319IVliYiJ9+vRh9uzZ5M6dW+lwLE6dOnWwtrbm4MGDbzwWrdZy61USVyITufgigSuRidx6lUSMUuf3gtkQs+pMlCzL+Pn5YWNjw2+//Wa2o1TeJzExkfPnz+Pp6Wm0NeM1Oq5HJWWqI/j7nPJfR+i2NfRf9leani8D16OSiNfojNpZHKB3797Ex8fTqFEjDh06ROHChY26vpDc5bp06dImM2rI0qQUiS9cuJAadeqle+5kaRdbKue2N/rPpmDaROJkokaPHs21a9cIDg5GpbLMf6Zz585RunRpHB0djbfm8wT+nYL+3NKT9mNnUbpGfaPF8F8SyXHVyJfN6GsPGjSIhIQEGjVqxMGDB8mfP7/RY8iqLly4wPz58zlz5oxFfjAyFc06fcYpjQu/nn+OJEmvzZ3UvnGEl/xnrVbmfqyGR3EaDj2Kw83FVpG5k4Jpssx3ZDP366+/smXLFo4ePUq2bMZ/MzUWJY7pbkQlpXlgr1ajwdoISatGTo5LicQJYNiwYcTHx9O4cWMOHDhAnjx5FIkjK9Fqtfj5+TF58mQKFSqkdDgW6Z+5kzrK1fdGi5Tu4ZMpvyuuvkziRlSSInMnBdMjEicTs2nTJn766SeOHDli8W9gISEhRm+tEJGgTf3/G34YSNTjB6z85jOsrKxo+MW37Jo7kXZjZxG8eDo5CxSh39Id/DGiN3fCjqNOTKCAWwXajv6FfKXKARD78gWbx3/FrVNHyVvcDbdPGry+3u3r7Jg2ioeXz+KYMzdNBoykctO2741LCd9//z1xcXE0adKEffv2kTNnTkXjsXTz5s3D3t4ePz8/pUOxSP+dOyllcgC6zP8/4LxKYuGlSKPPnRRMi0ibTcihQ4cYOHAgO3fupHjx4kqHY3DGbkUQrda+1oLg0x8X4JK/MD1mr2HC0btUbtoGgNun/mbo5qP0nr8RgLK1GvHtthB+CLpMwXKV2fB9/9Rr+P/0HSo7O0bvuUD7cXM4tX1t6mNJ8bEsG9iBKs3b8X3QZbpMXcz2n77jya2rb8Sm0cmKFqVKksTkyZPx8vKiefPmvHr1SrFYLN3t27f58ccf+f3337HK5Bu68DpLnTspmBbxU2siLly4QMeOHVm3bh3u7u5Kh2Nwr1694u7du1SoUMFoaz6N16JKQy1J437DsXVwxMbeAYCP23bDztEJla0djfuP4NG1iyREv0Kn1XJh304a9x+JrYMj+Ut/hGerT1Ovc+XQHnIWKMLHbbpirVJRsFxlKjZsxfm9/m+sqZIkIuI1+vtiM0CSJGbOnImnpyetWrUiNtZ4Y2GyClmW6du3LyNGjMDNzU3pcCyKpc+dFEyHOKozAffv36dFixbMnj2bRo0aKR2OUZw6dQp3d3dsbIxXbJmklV+7g+ZdXPL9U3Oi02rZM38y54N2EBv5DElK/qwR+/I56sR4dBoNOfL/MzMwR4EiEPY3AJGPHnD/wmkm1Cv1r+tp8Gj55h1UMjJJJtAjQZIk5s+fT58+fWjbti07duzA3t5e6bAsxooVK3jx4gVDhw5VOhSLo9TcSTtriUaFRbf3rEQkTgqLjIykefPmfP3113Tp0kXpcIxGiY7hb95Bw9vvZvrX350J3MylA7vo89smchYsSkLMKybWL40syzjmzIOVSsXLx+G4lkjePXj5+EHqa13yF6JE1Vr0+W3TB2OT3xGfEqysrFiyZAndunWjQ4cObNmyBVtbW6XDMnuPHz/mu+++Y8+ePRZ7p6xSlJ47WTS7jah5ykLEUZ2C4uPjadOmDc2aNWPYsGFKh2NUSiRO1m9Jkpxy5eXFg7vvfE1iXAwqW1uyueRCnRDH7l8npz5mZW1NhQYtCV40jaT4OJ7cusrpnRtSH/+oblOe3b3J6Z0b0arVaNVq7l8MI+LWm926pXfEpxRra2tWr16NjY0NXbp0QaNR9hjREgwePBg/P78scRRvTFlx7qSgLJE4KUSr1dKtWzcKFSrE9OnTlQ7H6Iw9aiU+Pp5b16+SlJT02t979f6afUtnMqFeKc4H7XjjdZ6tOpGjQBGmNq/ErPZ1KFqp6muP+4z8icS4WKY0rcCmcV9S1eefXUM7Ryd6L/iTc3u2MrVZJSY3rcCuORPRqBPfWEdCwtbadBInABsbG9avX09cXByff/45Wq2yd/6Zs61bt3L+/HnGjh2rdCgWJ6vOnRSUI8miss3oZFlm8ODBXLlyhYCAAOzsstYW76NHj6hQoQLPnz83SOO/ly9fcubMGcLCwjh9+jRhYWHcunUL95q1aPXzaqxUptfEzlqCARVy4WRjep9l4uPjadWqFcWKFWPJkiXiTrB0ioyMpGLFiqxfv566desqHY5FCY9Vs/Z6VJp7sxmSSoJubi4UEE0yLZ74DaiAqVOncvToUbZu3Zrlkib4p/GlPpKmJ0+esGvXLqZMmULHjh0pVaoUhQsXZvTo0dy8eRMvLy9Wr17Ny5cvObYvCAc706zVUVlJJpk0ATg4OODv78+1a9f48ssvxV1E6TR8+HDatGkjkiYDEHMnBSWICsU0iFZreRqvJUkro5VlrKXkYxVXB1W63+xWrFjB77//zrFjx3B2djZQxKYtI8d0sixz79691B2klP+Ni4vD09MTDw8PfH19mThxImXKlMHa2vqt13G1t+Z+rOnV67javz1eU+Ho6EhAQACNGzdm+PDh/PLLL2JMSBoEBwezZ88eLly4oHQoFsfQcyfTS8m5k4JxicTpLeI1OoMMgwwMDGTkyJEcPHiQAgUKGONLMUkhISF8+eWX73xcq9Vy/fr11xKk06dPY29vj4eHB56envTu3RtPT0+KFSuWrjfw0i62PIrTmMTWfgqVlByXqXN2dmbXrl00bNiQsWPHMmnSJKVDMmlxcXH07duX3377Lct+SDKk/86dBNg6+VucXQvQ6Ith3Dp5lA0/DGDUrnNGi2nvwmkEP7vHrk3rjbamYHwicfqX8Fg1IRHxXI9KQgK9DoMMDQ2lR48ebN++nbJlyxr2CzFhsixz8uTJ1B2npKQkLl269FqCdO7cOVxdXVOTpGHDhuHh4aGXAbSVc9tz6FFcpq+jTzLJcZmDXLlysXfvXurXr4+DgwOjR49WOiSTNXbsWGrWrEnLli2VDsUivW3upO/3yt5oo5MhNkncXWfpROLEv4dBJqGV0z0HEnj/MMgbN27g4+PDkiVL+OSTT/QauzmJjY3lr7/+AuCHH34gLCyMy5cvU6JEidTjtvbt2+Pu7k6OHDkMEoODygo3F1uuvjSNLX4JcHOxNaut/bx58xIcHEy9evVwcHBgyJAhSodkckJDQ1mzZg3nz59XOhSLpfR8x3dJMJWiK8Fgsnzi9N9hkJn132GQdVw0dG7WjIkTJ+Lj45P5BcxEZGQkYWFhrx233blzh3z58uHi4kLVqlXx8/OjcuXKZMuWzaixVXd1eOunVSVYS1DD1UHpMNKtQIECBAcHU79+fezt7RkwYIDSIZmMpKQk+vTpw8yZM8mbN6/S4Vikk+cu8GvvfoRfvYCzawGaffkD5es3589xg3FxLUjTQW/uhB5cMZdj634nITYa57z5aTNyGqVr1EOn03Fo5TxCt64hPjqK0tXr0nb0dLK5JA+6vnfuJH/NHEvEravkKFCE1sMnU/Lj2gC8eHiXTeO+5OGVcxSt9DF5ipVCBmLUOpO92UPIvCybOMmyzL6HsQbrNquVk4/xdj9S0+unhfh1aKz/RUzEo0eP3qhHevbsGVWqVMHT05PGjRszYsQIPvroI0aMGEHBggUVfaMt6GhDKWdbbvx/h1Ep1hKUcrE129uXixYtSnBwMF5eXjg4ONCzZ0+lQzIJ06ZNo0iRIllqEoAxqdVq2rdtQ8WWXei14E/uhp1g1dDuDF4T9M7XPL1zg783LGXQmr04581PZPg9dP/vS/b3+t+5tD+Qvr9vxzFnbnZMG832n76jy9TFREU8YsXXXek0aT5lajXiZsgh1gzvxdAtx3DKmYf1o/tTtPLH9F7wJ/fPn2LF112p6OVNRLwGJxvTr1sUMiZLJk4pwyCNMddIZWePTRkPAu/F4F3UyazvRJJlmTt37rxxZ1tSUlLqUVvHjh2ZMmUKpUuXfuudbSEhIUyZMkWB6F/XvKgTCy9FolUwc1JZSXgXMe8ZVyVLlmTv3r00aNAAe3t7OnfurHRIirp8+TKzZ8/m9OnTZv2zbsqOHz9OXGwsDXp/hQYrSlWvS7m6TTm7a8s7XyNZWaFJSiLi1lUcc+QmZ8GiqY+d2LQSn+9+wiVf8szJRv2G83NLD7QaDWcC/qRs7caUq9MEALeaXhT+qApXjwRR6uM6PLwUht/CTahs7ShRtRYf1WsGYBJzJwXDyZKJkxgG+WFarZarV6++liCFhYXh6OiYWrTdt29fPD09KVKkSJreJNRqNWfPnqVq1aoffK6hOaisaFXMCf870YqMarCxglbFnLA3o9qmdylbtiy7d++mSZMm2NnZ4evrq3RIitBqtfTp04eJEydStGjRD79AyJDw8HDyFSyU3Ij1/z+7OQsU5tXTR+98TZ6iJWn17Y8ELZrGk5tXKfNJA1oOm4Rz3vy8fPyANd/2SB3gDWBlZU3Mi6dEPnrAhSB/rhzanfqYVqOhZLU6vHr6GIfsObB1cEx9LEeBwkQ/CTeZuZOCYWS5xEkMg3xTYmIiFy9efG0n6fz58+TPnz91J+m7777Dw8MDV1fXDK9z/vx5ihcvTvbs2fUYfca5udjhnltt9O8HGytwz21vct8HmVGpUiUCAgLw9vbGzs6OFi1aKB2S0S1YsABra2v69++vdCgWrWDBgjwJf4hOpyOlh/PLxw/JU7QUkY/uvfN17t7tcfduT0JMNFsnDyNwzkQ+/XEBLvkK0n7cHIq713jjNTnyFcSjZUfajZn1xmOR4feJj35JUnxsavL08vFDrCXJpOZOCvpn/h9300EMg4SYmBiOHj3Kr7/+Su/evfHw8CBHjhz06NGDQ4cO4ebmxk8//cTDhw+5ceMGGzduZNSoUTRr1ixTSRP80zHclDQs5MhHOewwVh2njRWUz2FHw0KOH36ymfH09GT79u307NmT4OBgpcMxqrt37zJhwgQxksYIatSogYODAwdW/IpWrebWyaNcPrSbys3evdP59M4NboYcRpOUiMrODhs7eySr5OSmRvue7Jk/hcjw+wDERD7j0oFAANxbdOTyod1cO7YPnVaLOjGBWyePEvUknJwFi1DoI3eCFk5Do07iTtjx1J0pU5s7KehXltpxMqVhkL4lDN8Q78WLF28Ubd+7d48KFSrg6elJ9erV6d+/P5UqVcLBwfB3doWEhJhc4iRJEt5FnbCzlgh5FI1kwDl2KTtNDQs5Wmz9S82aNdm0aRMdOnRgy5Yt1KlTR+mQDE6WZfr168fQoUOzdI82Y7G1tWXTtu107tOfoGWzcclbgE4T5+Nawu2dr9EkJbJr3iQibl/DWmVD0crVaDdmJgC1uvZFRmbZoI68evoYp1x5qdykDeW9vMmRvxDdZ65m15wJrB/dDysrawpX9KDtqF8A6DxlIX+OHcwkLzeKVq6GR8tOJMa8wtUhS721ZjlZZsivJQ+DlGWZ8PDwN5KkyMhI3N3dU2uSPDw8+Oijj7CxUeYurkqVKrFixQqTqHH6r1OnTjFowjQ6TfwVLZJe77azlpILwVsVc7Ko47n32bt3L926dWPnzp0mlyzr2+rVq5kxYwahoaGK/WxlRTPOPEUtm94HEDtriSGVcysdhmBAWSZx2nb7lUk1PSybw5a2Gdh1kmWZW7duvXFnm06nS02OUhKlUqVKmcyxQUxMDPny5SMyMhJbW9O6TTcxMZGqVasyatQo2n3aJdPNUFNI/NNyoHkRJ7NqcqkPO3bswM/Pj927d+Pu7q50OAYRERGRWt9lih8ILIksy5w+fRp/f3/8/f2p9c0UilRO38xLYyjiqKJbmRxKhyEYUJbYTzTXYZAajYYrV668cWebi4tLanI0cOBAPD09KVSokEkf/5w+fZpKlSqZXNIEMGHCBNzc3OjatSuSJOFb0plHsWpOvGP8zoeopOR/YzcXW2q4Ophtn6bMat26NfPnz8fb25ugoCAqVKigdEh699VXX9GjRw+RNBlIQkIC+/fvx9/fnx07duDo6EibNm2YN28e1qXcOfokwSROEVKYy9xJIXOyROL0tmGQ/zarQx3ajPw5tRvs2+h7YKT0/7hq5Evump2QkMCFCxde20m6cOEChQoVSt1J+v777/Hw8CBPnjx6icGYTLG+CZLjWrp0KefOnXst8SzgaEPbEjbvHfgsk/zvmJGBz1lFhw4dSExMpGnTphw4cAA3t3fXoZgbf39/Tp06xbJly5QOxaI8ffqUgIAA/P39CQoKokqVKvj4+BAcHPxaDVm8RseRJwkKRvomc5o7KWRclkicPjReY8imI8YL5v80Mhy8dJvfvvuFsLAwrl27RpkyZVJ3krp27UqVKlUsZqp6SEgIrVu3VjqM1yQkJNCjRw/mzJlDvnz53vocB5UVNfJlS01wY9Q6IuI1JGlltLKMtSRhay3h6qASIxbeoVu3biQkJNCoUSMOHTpE8eLFlQ4p06Kiohg0aBCrV682+sggS3T16tXUI7hz587RpEkT2rRpw6JFi975QVHMnRSUkiUSJ1MdBql2cKZWrVoMHjyYihUrYm9vuZ9UQkNDmTRpktJhvGbcuHFUqFCBTz/9NM2vcbKxEqMUMqBPnz6pydPBgwcpXLiw0iFlynfffUeLFi3w8vJSOhSzpNFoOHbsWGqyFBcXh4+PD99//z1eXl5p/l0o5k4KSrD4xClarf1gC4KfW3rSfuwsinvUJHDORM7v3Q5ApSZt8P56LCrbf+6E2r90Fkf+WIitgyNNB43Go0UHAK4c2UvgrPG8fPIQe8fs1O7Wn3qfD3rvurb2DnTt5WfxOxURERFERkaa1DHN33//zcqVK984ohMMZ9CgQcTHx6cmT/nz51c6pAw5ePAgO3fu5OLFi0qHYlaio6PZvXs3/v7+BAQEULRoUXx8fFi/fj0eHh4Z+jkUcycFJVh84vQ0XotKktLUAn//0lncP3+Kr9bvB0li9ZDP2bdkJk0HjgIg5nkEsS9fMGrXOe6dP8mKL7tSuLw7eYuXZsvEb+jy0xJKeH5C/KuXvHh494PrqSQpSwyDDA0NpVq1aiZzh198fDw9e/Zk3rx5mW7qKaTPt99+S1xcHI0bN+bAgQNmV68XHx+Pn58fCxYswMXFRelwTN79+/fZsWMH/v7+HDt2jFq1auHj48PkyZMpUqSIXtYQcycFYzONdzIDStKmlPF+2JnAzTTsOwynXHlxypmHRn2/JeyvP197TtOBI1HZ2lGyam3K1W3Muf/vTlmpbIi4dY2EmGgcnHNQ6KMqH1xPRs4SwyBDQkKoVs10bhv+4YcfUgcSC8Y3ZswYWrduTdOmTYmMjFQ6nHSZMGECnp6e+Pj4KB2KSUppGTB+/PjUm1qOHz+On58fDx48YNeuXQwcOFBvSRP8M3dSqY17dUI8peMfWsTcSSFtLP5fOj3DFl89fUzOAv/8QOcoUITop49T//zGQMf8/zze7ZflXD0axM8tPVjs58Pds6EfXE9OZ3zmypRGrRw5coS1a9fy66+/Kh1KliVJElOmTKFevXp4e3sTHR2tdEhpcvr0aZYvX87cuXOVDsWkJCYmpiZERYsWpXPnzsTExDB79mweP37MqlWr6NChg0FvdEmeO2lv9OTJxgryqyPp07oxixcvNu7igmIsPnFKz7BF57z5iXx0P/XPLx8/IHvef+owUgY6vu3xIhU8+HzWan4Iukx5rxasG+n3wfWkdMZnjmRZNplWBLGxsfTq1YsFCxaY3RGRpZEkiVmzZuHu7k6rVq2Ii4tTOqT3UqvV9OnTh19++eWdd2BmJc+ePUtNiPLly8fkyZMpUaIEQUFBXLt2jenTp1OvXj1UKuNVgyg1d9KvbiWOHDnC7Nmz6du3L4mJicYJQFCMxSdOttYS0nu7OP2jSjNf9i+ZRUzkM2Ijn7Pv9+mpxd8pUgY63j79N1cO76VSYx806iTCAjaREP0Kaxsb7JyckKQP/6eVkCx+GOTt27ext7enYMGCSofC6NGjqV69Or6+7x4GKhiPJEksWLCA4sWL06ZNGxISTKsnz79Nnz4dV1dXunfvrnQoivl3QlSqVCm2b99O69atuX79OocPH2b48OGKzupLmTtpjJ2nlLmTzYs6IUkSZcqU4cSJEzx79gwvLy/Cw8MNG4CgKIsvDs/rYI0mjcdhDfyGkhAbzdxP6wNQsbEPDfyGpj7ulNsVh+wuTG1WCRt7B9qO/gXXEm7JidNfG/H/eSSyTkueYqX5dPJvH1xPI8sWPwzSVOqbDh48yKZNmzh3Tj8NTAX9sLKyYunSpXTr1i11MLCpdZe/evUqM2bM4OTJk1nqDkytVsuxY8dSi7ujo6Px8fFh1KhRNGjQwCTbp0iSRKPCThTNbsPOu8lD3Y01dzJ79uxs2rSJqVOnUq1aNf78809q1aqlv8UFk5ElZtXNOvecxPf89PzUwp1PJy2gRFXjfpNnhWGQw4YNI0+ePIwaNUqxGGJiYqhSpQqzZs0SRb0mSq1W07FjR1QqFevXrzfqEc/76HQ6vLy86NChA1999ZXS4RhcTEwMe/bsYfv27QQEBFC4cGF8fHzw8fHB09PTrBLHeI1OsbmTf/31F7169WLSpEn07dvXrP67CR9m8Ud1AK721u98LOVYLmfBokaMKNn74rIUplDfNHLkSOrUqSOSJhNmY2PDhg0biI2NpUePHmi1ptG0dtGiRWg0GgYNen9PNnP24MEDfvvtN7y9vSlYsCCLFi2ievXqnDp1irCwMCZMmEDVqlXN7s3fQWWFb0lnurm5UDaHbfJuUTq/BJWUnDCVzWFLNzcXfEs4p6kzeMuWLTl69Chz584VdU8WKEvsOJ14EsfhR3FvdJe9fzGMZQM6UL3d53h/M86oMakkqFvgn1Eelkij0ZAjRw4ePnyoWM+bffv28fnnn3P+/Hly5sypSAxC2sXHx9OyZUtKlizJ4sWLFe39df/+fTw9PTl48CDly5dXLA59k2WZM2fOpHbtvnPnDi1atMDHx4dmzZpZzJin/1Ji7mR0dDQ9e/bk4cOHbN68mUKFCun1axKUkSUSp3iNjl8vvFC0s+x/WUswuGIui55rdPbsWT799FOuXLmiyPrR0dFUrlyZ+fPn06JFC0ViENIvJiaGZs2a4eHhwbx58xTZ6ZBlmdatW1O9enXGjh1r9PX1LTExkQMHDqQmS3Z2drRp0wYfHx9q165tMkejxmSsuZOyLDN16lTmz5/Pxo0bqV373cPkBfOQJX5axDBIZSjdv2n48OE0aNBAJE1mxsnJiYCAABo3bsyIESOYNm1ahpOnaLWWp/HadL85rl+/nrt377Jly5aMfhmKe/78OQEBAfj7+7N3714qVKiAj48Pe/bsoVy5cmZ39KZvxpo7KUkSo0ePxsPDA19fXyZMmED//v2z/H9/c5YlEicQwyCVoGR90969ewkICBB30ZkpFxcXdu/eTYMGDXBwcGDixIlpet37jmNSfOg45tmzZwwZMgR/f3+Tu8PvQ65fv566q3TmzBkaNmyIj48P8+fPF+OFFObt7c2xY8do27Ytp06d4tdffzXJOxOFD8sSR3Uptt56ZRLDIEu72OJbwjLrCP7N3d2dRYsWUaNGDaOu++rVKypVqsTixYtp1qyZUdcW9CsiIgIvLy+6d+/+3jszw2PVhETEcz0qCQnS9QFJJSXfceXmYkt1VwdG9OtFvnz5mDFjRqbjNzStVsvx48dTk6WoqKjUu+BSkk7BtERHR9OrVy/u37/Pli1bRN2TGcpSiVO8RsfCS5HvbU1gaHbWEgPK57T4uUZxcXHkyZOHFy9eGP1T1RdffIEkSWIEgoUIDw+nfv36DBo0iG+++ea1x/R9yzk6LTdPHGTqZy3I7WyaQ1tTWgbs2LGDv/76i4IFC77WMsBUhmkL7ybLMj/99BPz5s1jw4YN1K1bV+mQhHTIUokTwPWoRPzvRKPWGX9tGyvwKZ79jcZplujo0aN88803hIZ+eGafPu3atYv+/ftz7tw5i707KCu6d+8e9evXZ+TIkfTr1w9I/lk2RJNDSdZhq7J+a5NDpTx8+DC1EeWRI0eoWbMmPj4+tG7dmmLFiikdnpBBu3btokePHowbN44BAwaIuiczkWVqnFIkD4NUc+Z5glGTp5QW/abyi9jQlKhvevnyJV988QXLly8XSZOFKVq0KEFBQXh5eWFnb0+RRu0N9jMsS1YkamX870TjnltNw0KORn9Dk2WZs2fPph7B3b59G29vb3r27Mm6desUa+8h6Ffz5s05evQovr6+nDx5kgULFoi6JzOQ5XacIPmXUuC9GC6/TDRK8pQyDDJlrlFW0KVLF5o1a0bPnj2NtmavXr1wcHBgwYIFRltTMK7Ll68wedtBKjVtg2xl+M99NlbwUQ47vI3ws5uYmMjBgwdTkyUbG5vXWgbY2NgYdH1BOTExMfTu3Zs7d+6wZcsWChcurHRIwntkycQJkpOnfQ9jDb7zpE6Ip3JOG3zK5ssySRNA6dKl8ff3N1rjwL/++ovBgwdz/vx5nJxMszZFyLzgBzGcfhqH1ohDD1J2ixsV1v/31YsXL1JbBuzZs4fy5cun1it99NFHWep3RlYnyzLTpk1jzpw5rF+/nnr16ikdkvAOWTZxSmGoOomUYZCJobvZungOhw4dws4uaxzTPX/+nBIlShAZGYm1teHHykRGRlKpUiVWr15NgwYNDL6eoAxLqU+8ceNG6q7S6dOnU1sGtGzZknz58ukhWsGc7d69m88//5wxY8YwaNAgkTyboCyfOIFhh0HaW0t07NiRnDlz8vvvv+srZJO2a9cupk2bxr59+4yy3ueff46Liwvz5s0zynqC8ZnKHbH9y+dMd+NarVbLiRMnUpOlyMhIWrdujY+PD40aNRItA4Q33Lx5E19fX6pWrcpvv/0m6p5MjLhvFcMOg5QkieXLl3Ps2LEskziFhIRQrVo1o6y1fft2jh07xk8//WSU9QRl7LqXvCusJI1OZtf9mDQ9NzY2lm3bttG7d28KFCjAgAEDsLGxYeXKlTx8+JDFixfTqlUrkTQJb1WqVCn+/vtv4uLiqFu3Lvfv31c6JOFfxI7TWxhiGOS1a9eoU6cOO3bsMHpDSGNr3bo1PXv2pH379gZd5/nz51SqVEn0QbFw4bFq1l6PMomu/yoJurm5UMDxzULt8PBwduzYwY4dOzh06BA1atRIbRlQvHhx4wcrmD1Zlvnll1+YNWsW69evp379+kqHJCASpzTR1zBIf39/Bg0axMmTJy22lkGWZfLnz8/JkycpUqSIQdfq2rUr+fLlY9asWQZdR1DWttuvTGrOZNkctrQt4Ywsy5w7dy71CO7mzZt4e3vj4+ND8+bNRcsAQW/27NlD9+7d+eGHHxg8eLCoe1KYSJyMbNy4cezfv5/g4GCLvL347t271KhRg0ePHhn0h3vLli2MHDmSM2fOkC1bNoOtIygrXqPj1wsvFB2T9F+SrCN87XS2/bkBa2vr1JYBderUscifacE03Lp1C19fX9zd3Vm4cKE45lWQqHEysnHjxuHs7My3336rdCgGkdL40pBJ09OnTxk0aBDLly8XSZOFO/c8gbR+J43yzMuze7cMGg+AOimJ/J71CAgI4ObNm8yaNYsGDRqIpEkwqJIlS3Ls2DESExOpW7cu9+7dUzqkLEskTkZmZWXFmjVrCAgIYM2aNUqHo3ehoaEG7xg+ePBgunXrRu3atQ26jqC8G1FJitc2BS2cxobvB6T+WWVnT9GP61ChQgVxZCIYlaOjI+vWraNz587UqFGDAwcOKB1SliQSJwXkyJGDrVu3MmTIEMLCwpQOR68MPWpl48aNnD17lkmTJhlsDcF0RCRolQ7hrUw1LsHySZLEt99+y6pVq/j000+ZM2cOouLGuETipJCKFSsyf/582rVrx/Pnz5UORy+0Wi2nTp3i448/Nsj1nzx5wldffcWKFSvE+X4WEK1OvqP15Pa1rPy6W+rfT29TnT9G9E7980/eVQi/eh6AmyGHmN6mOhPqlWL71BGvvaGc3PYHM9vVYkL90iwb2JHI8H9u8d7xy2h+8q7C+LolmNe1EbdP/w3A1aPBHFg2m3N7tzGudjHmfOoFJLcmiFGiE6cg/F+TJk04fvw4y5cvp0ePHsTHxysdUpYhEicFderUiQ4dOtClSxe0WvP/BHvlyhXy589Prly59H5tWZYZMGAAPXv2pGbNmnq/vmB6nsZrUUkSJavW4k7YcXQ6Ha+ePkarVnPv3EkAXjy4Q2JcLPndKgBw+dAeBq3Zy9cbDnJ+rz/XjiU3Yb10IJD9y2bz2fQV/BB8heKeNVk/ul/qWoXLe/Dluv2M2X8dd+/2rP2uD+rEBMrWboRX72+o3KQtE47e5esNBwBQSRIR8Rrj/gcRhP8oUaIEx44dQ6PRUKdOHe7evat0SFmCSJwUNnXqVHQ6Hd9//73SoWSaIY/p1q9fz9WrVxk/frxBri+YniRtcue0XIWLY+foxKOr57l9+m/cPmmAc978RNy+zq1TxyjhURMrq+RfZV69vsIhuws5ChSmZLXaPLp2AYATm1bg1ftrXEuWwVqlwqv3EB5du5C66+TRsiOOOXJhrVJRt/tANElJPLt7452xycgkmdKtfkKWlS1bNv744w+6detGzZo12b9/v9IhWTzDjxcX3kulUrF+/Xo+/vhjqlWrZvCmkYZkqMTp8ePHfPPNN+zcuVOMHshCtP86ZivhWYtbp47x/P5tSlSthUN2F26fOsa986GUqFor9XlOuV1T/7+NvQNJcbEAvHz0gJ2/fE/AzHGpj8uyzKunj8hZsAiHVs3n5LY/ePXsMRISibHRxEa+eGds8n/iEwQlSZLE0KFDqVKlCl26dGHkyJF8/fXX4uYFAxGJkwnIkycPmzdvxtvbm48++ojy5csrHVKGhISE0L17d71eU5Zl+vfvj5+fn9HGuAimwfpfv/RLVK3FlUO7eRF+jwa9v8EhuzNnAjdz79xJPvnU74PXcslXEK8+Q/Bo0eGNx26f/ptDK3/Fb+FmXEuVw8rKign1S5M6tfItbz7Sf+ITBFPQqFEjjh8/jq+vL6dOnWLRokWiZYsBiKM6E1G1alV++eUXfH19iYqKUjqcdEtISODy5ct4eHjo9bp//PEHN2/eZOzYsXq9rmD6bK0lpP93cSpRtRY3Tx5BnRCPS76CFPf4hGvH9hEX9YKCZSt98Fo1OvTk4PI5PLl5BYCE6Fec37sdgMS4GKysrXHMmRudVkPw4ukkxkanvjZ77rxEPrqHTvdPMbhE8vQAQTA1xYsX5+jRo+h0OlH3ZCAicTIhPXr0oEmTJnTv3v21X9Lm4MyZM5QrV06vd7uFh4czdOhQVq5ciZ2dnd6uK5iHvA7WaP5/HJa3WCnssjlSwiP5xgB7p+zkKlSMYlWqY2Vt/cFrVWjYkno9vmTdqL6Mr1uC2Z3qcvVoMABlPmlImVoNmeFbk2ktPVDZ2uGSr1Dqays29gFgUoMyzOvaEACNLOPqIDbsBdOULVs21qxZw2effUaNGjXYt2+f0iFZFDFyxcQkJSXRsGFDmjZtala7LHPnzuXSpUssXLhQL9eTZZnWrVtTtWpVJkyYoJdrCuZn1rnnJJpgEbadtcSQyrmVDkMQPmjfvn107dqVESNGMGTIEFH3pAdix8nE2NrasmnTJhYvXsxff/2ldDhpFhISotcapJUrV/LgwQOLuNtQyDhX+w/vJinBVOMShP9q2LAhJ06cSN2BiouLUzoksycSJxOUP39+/vzzT3r16sX169eVDidN9Dlq5cGDBwwfPpyVK1dia2url2sK5qm0iy0qE/uArJKS4xIEc1GsWDGOHDmCJEnUrl2bO3fuKB2SWROJk4n65JNPmDBhAr6+vsTExCgdzntFRkYSHh6ul7sBZVnGz8+PL7/8kipVqughOsGcVc5tj6kd1MkkxyUI5iRbtmysXr2aHj16ULNmTYKCgpQOyWyJxMmE9e/fn+rVq9OnTx+TnkV08uRJPDw8sE5Dke6HLFu2jIiICEaNGqWHyARz56Cyws3FFlPZdJIANxdbHFTiV6dgfiRJ4ptvvmHdunV0796dGTNmmPR7i6kSP/0mTJIkFixYwK1bt5g+fbrS4byTvhpf3rt3j5EjR7Jy5UpsbGz0EJlgCaq7OmAqd/5bS1DDVcxJFMxbgwYNOH78OGvXrqVbt26i7imdROJk4uzt7dm8eTMzZ8402a1VfdQ3ybJMnz59GDJkCJUqfbgvj5B1FHS0oZSzreLJk7UEpVxsKeAoknrB/KXUPalUKmrVqsXt27eVDslsiMTJDBQtWpS1a9fy2WefmVwzM1mWOXHiRKYTp8WLF/Py5UtGjBihp8gES9K8qBMqK2UzJ5WVhHcRJ0VjEAR9cnBwYOXKlfTu3ZtPPvnEZD+cmxrRx8mMzJw5kz/++IMjR47otdFkZjx48AAPDw8iIiIy3B/k9u3bVKtWjUOHDpntuBnB8K5HJeJ/Jxq1Ar1hbazAp3h23FxEI1bBMh04cIAuXbowbNgwhg0bJvo9vYfYcTIjQ4YMoUyZMgwYMMBkCvpSjuky+kOm0+no06cPw4cPF0mT8F5uLna457bHxsi/tWyswD23vUiaBIvm5eXFiRMnWL9+PV27diU2NlbpkEyWSJzMiCRJLFmyhLCwMBYsWKB0OEDmC8MXLlxIXFwcw4YN02NUgqVqWMiRj3LYGS15srGC8jnsaFjI0TgLCoKCihYtyuHDh7G1taVWrVrcunVL6ZBMkkiczIyjoyNbtmxh4sSJHDlyROlwMpU43bp1i7Fjx7JixQpUKjH3S/gwSZLwLupklJ2nlJ2m5kWdxLGFkGU4ODiwYsUK/Pz8+OSTT9i7d6/SIZkcUeNkpgIDA/Hz8yM0NJSCBQsqEoNOpyNnzpzcvHmTPHnypPu1DRo0wMfHR+w2CRlyPSqRnXdj0Ohk9DnOzlpKLgRvVcxJHM8JWdrBgwfp3LkzQ4YMYfjw4eIDxP+JxMmM/fjjjwQEBHDgwAFFRpNcuXKFFi1aZGg7d+7cuWzYsIFDhw7ppXGmkDXFa3TsuhfDzVdJaGUy1WVc4p+WA82LOIkml4IA3L9/n3bt2lGyZEmWLVuGo6M4tha/GczY6NGjcXV15euvv1Zk/Ywe012/fp2JEyeyfPlykTQJmeKgssK3pDPd3FwomyO511N6Z9uppOSEqWwOW7q5ueBbwlkkTYLwf0WKFOHw4cNky5aNTz75hJs3byodkuLEbwczZmVlxapVq9i/fz/Lli0z+vohISFUq1YtXa/RarX06tWLMWPGUKZMGQNFJmQ1BRxtaFvCmcEVc1G3QDaKOKqws5awlsDOSsLWKrlmydYq+c/WEthZSxRxVFG3QDYGV8xF2xLOormlILyFvb09y5Yto1+/ftSqVYvdu3crHZKixFGdBbh8+TL16tUjICAg3YlMZlSvXp0ZM2ZQt27dNL9m5syZbNu2jQMHDmBlJfJ2wbBi1Doi4jUkaWW0soy1JGFrLeHqoMLJ2H0NBMECHDp0iM6dO/P1118zYsSILFn3JBInC7F161a++eYbQkNDcXV1Nfh6iYmJ5MyZk6dPn6b5zPvq1avUrl2b48ePU7p0aQNHKAiCIBjCgwcPaNeuHcWLF2fZsmU4OWWtjvriI5eF8PX15bPPPuPTTz9Fo9EYfL1z587h5uaW5qRJq9XSs2dPxo8fL5ImQRAEM1a4cGEOHTqEk5NTlqx7EomTBZk4cSJ2dnZ89913Bl8rvfVNM2fOxN7enoEDBxowKkEQBMEY7O3tWbp0KQMGDKBWrVrs2rVL6ZCMRiROFsTa2pq1a9eybds21q1bZ9C1UkatpMWlS5f4+eefWbZsmahrEgRBsBCSJDFw4EA2bdpE7969mTp1qsmMAzMkUeNkgc6ePUvjxo0JDg6mcuXKBlmjfPnyrF27Fnd39/c+T6PRUKtWLXr37k3//v0NEosgCIKgrAcPHtC+fXuKFi3K8uXL9Vr3FK3W8jReazI3eYjEyUKtXbuWMWPGEBoaSq5cufR67aioKAoVKkRkZCQ2Nu+/fXvq1KkEBwezd+/eLHn3hSAIQlaRkJDAoEGDOHHiBNu2bctwPWu8Rse55wnciEoiIkGLRiejkiTkf7W4lZDQyDIqKwlXe2tKu9hSObe9UXqwicTJgg0ZMoQrV66wc+dOvTaa3LdvH2PGjOHo0aPvfd6FCxdo0KABJ0+epFixYnpbXxAEQTBNsiyzcOFCxo0bx8qVK/H29k7za8Nj1YRExHM9KgkJ0KQjO1FJyZMD3Fxsqe7qQEED9mQTBScWbNq0acTHxzNu3Di9Xjct9U1qtZqePXsyZcoUkTQJgiBkEZIkMWDAALZs2YKfnx9Tpkz5YN1TvEbH1luvWHs9iqsvk8cnpSdpguTna2W4+jKJtdej2HrrFfEaXSa+kncTiZMFs7GxYePGjaxatYpt27bp7bppGbXy888/kzt3bvz8/PS2riAIgmAe6tSpQ0hICP7+/nTo0IHo6Oi3Pu96VCILL0Vy41USmkzOm4Tk12tkuPEqiYWXIrkelZjJK75JHNVlASEhIbRs2ZLDhw9Trly5NL/uXQV53nVqELB1E6VKlXrr61KK00+fPk2RIkX09WUIgiAIZiYxMZHBgwdz7Ngxtm3bhpubG5B8pLfvYSxnniegNszGEJA8ask9tz0NCznqrc5WJE5ZxNKlS/nll18ICQnB2dn5rc9JS0GeLMvEJyTi6OCAq8ObBXlJSUnUqFGDr776il69ehnlaxMEQRBM26JFixgzZgwrVqzA29ubwHsxXH6ZaNCkKYWNFXyUww7vok56SZ5E4pSF9O/fnydPnrB58+bX+inpsyBv8S+TCQ0NZefOneIuOkEQBCHVsWPH6NSpEwPnrMSuVBXURsw+UnaeGhXOfJsEkThlIYmJiXh5edG6dWtGjx5NvEbHrnsx3HyVXIyXmW8ECZDQceXwXn7w9aJkkUJ6iloQBEGwFCduPWJvhBaVnb3R17axAp/i2XFzscvUdUTilMU8fPiQatWqMeuPzYTnLoNGJ6PV53eAToudjYpWxZwy/c0pCIIgWI54jY6FlyJJ1OubTvrYWUv0L58zU/2eROKUxciyzOqQa9zVOWBjn81g6xiiIE8QBEEwX1tvveLG/084lGItQWkXW3xLvL3WNy1EO4IsRJZlAu/F8NQ+t0GTJgC1Ds48TyDwXkyWmF0kCIIgvFt4rDq1LERJWhluRiXxKFad4WuIxCkL2fcw1mh3MUBy8nT5ZSL7HsYaZ0FBEATBJIVExCueNKXQynAiIj7DrxeJUxZxPSrR4P0y3iZl58kQTcgEQRAE0xev0XE9KinTzS31RQauRyVluLO4SJyygHiNjp13Y4yeNKVQ62Dn3RiDtb8XBEEQTNe55wlkpNI1MvweozzzotVo9B6TRHJcGSESpyxg170YNDplc32NTmbX/RhFYxAEQRCM70ZUUrpnzxmaRk6OKyNE4mThLKkgTxAEQTA/EQlapUN4q4zGJRInC2dJBXmCIAiCafv5558pVKgQ2bNnp2zZsuzYvYeEhAR2/PI9U5pWZErTiuz45Xs0Scl1r7M71uXywd2pr9eq1UxqWJbwK+dS/+7U9rX/f20FDq2an/r3Op2OA8vn8ItPNSY2KMPa7/oQFxWZ+vgfI3ozuUl5xtcryaI+rXly80rqY3+OG8yfk4fTrEULsmfPTo0aNbh582aavkaROFkwSyvIEwRBEEzX1atX+fXXXwkNDSU6Oprdu3fjlK8I+5fO4v75U3y1fj9fbTjAg4th7FsyEwCPlp0IC/jzn2scDSJ7nnwULFc59e9unjzCt9tP0Hv+nxxaMY8bJw4C8Pf637m0P5C+v29n9O7zOGTPwfafvkt9Xdlajfh2Wwg/BF2mYLnKbPi+/2vxnt29jf7DvycyMpLSpUvz/fffp+nrFImTBctoQZ4hZaYgTxAEQTBd1tbWJCYmcunSJdRqNcWLF6dAsZKcDthMw77DcMqVF6eceWjU91vC/kpOljxaduTq0SASYqIBCPtrIx4tOr523UZ9h2Pr4Eh+t/JU9enC2V1bADixaSVNB43GJV9BVLZ2NOo3nAvBO1KLyT9u2w07RydUtnY07j+CR9cukhD9KvW6FRu24CP3j1GpVHTr1o0zZ86k6etUZfY/lGC6TLkgr0Y+wzbgFARBEIyrdOnSzJ49m/Hjx3Px4kWaNWtGvzFTiHr6mJwFiqQ+L0eBIkQ/fQyAc978FKtSnQvBO6jQsCVXj+6j1fApr103R/6C/3ptYR7fuATAy8cPWPNtDyTpnz0gKytrYl48JXtuV/bMn8z5oB3ERj5LfU7sy+fYZ0/uGp49tyva/zdozpYtGzExabuBSSROFszSCvIEQRAE09a1a1e6du3Kq1ev6NevH7Mm/oBL3vxEPrpPvlLlgOSEJ3ve/Kmv8WzdmZNb16DTaila+WNcXAu8ds2Xj8NxLeH2////EOf/v9YlX0Haj5tDcfcab8RxeudGLh3YRZ/fNpGzYFESYl4xsX7p1yZZSIB1BkaCiaM6CxWt1qa5BUFKcd24OsWZ1b42F/f9lfpYyJbVzGxXK/Wxh5fPAhB+5RzzujZkXJ3irP3Oj3Ujv2DP/CnvWuI1Gp1MjFJNpQRBEASDuHr1Kvv27SMxMRF7e3scHBxQWVvj0bwd+5fMIibyGbGRz9n3+3Q8WnRIfV0FL28eXjnHsXWL8WzV6Y3r7lsyg6T4OJ7cvMIp/3VUatoWgBrte7Jn/hQiw+8DEBP5jEsHAgFIjItBZWtLNpdcqBPi2P3r5LdELGFrnf7ESew4Wain8VpUkpS6Dfk+uQsXp9/SHTjlduXC3u1s+GEg324/wd0zJwheNI3PZq6icHl3nt+/jbXKBo06idVDe1C7az8++bQPlw4Gsn50P+r3+DJNsakkiYh4DU42tpn9MgVBEAQTkZiYyMiRI7l8+TI2NjbUqlWLmfN/Y9NjidiYV8z9tD4AFRv70MBvaOrrbOwdqNioFWd3baVCw5ZvXLekZy2mt6mOLOuo230gZT5pAECtrn2RkVk2qCOvnj7GKVdeKjdpQ3kvbzxbdeL63/uZ2rwS2Zxz0mTgSE78ufy16+qQcXVIfxokyWICq0W6EplIwL1okjKwsTO3sxeN+3/H8Y3LKFunMbW79nvt8dunjrFuVF9G7T6P9P9tzt96tqBUtTo0HTT6g9e3tYIWRbNTLqdd+oMTBEEQzMqsc89J/EBfnODF03l29yafTv7NSFGBnbXEkMq50/06seNkodKy05Ti9M4NHFnzW+p2Z1J8LLEvnxP1JJxchUu88fxXz57g7FogNWmC5IK9tJLTGZ8gCIJgvlztrbkf++6xKXFRkZzc9gedJs1/53MMwdXeOkOvEzVOFiqtBW+R4ffZMmkoPt/9xJj91xh36GZyAZ8s45KvIC8e3H7jNdnzuPIq4tFrRXZRjx+kObaMFuQJgiAI5qe0iy2qd/zKD9mymp+83SlTuxElqtYyWkwqKTmujBCJk4WytZaQ0tDFKSkhDkmScMyZB4CT29emdlet5vsZh1cv4OGls8iyzLN7t4gMv0/RytWwUqk4tm4xWrWaC8E7uX8xLM2xSRksyBMEQRDMT+Xc9u9sxFy9XXcmHruL7/fTjRqTTHJcGSGO6ixUXgdrNGk4DstXsix1PhvAbz29kays8GjZiWJVqgNQqUkb4qIiWf99P15FPCJnwaJ0mjSfnAWL8Nn0FWyZNIQ9C6ZStnZjKjR4s6DvXTRyxgryBEEQBPPjoLLCzcWWqy9NY5KFBLi52OKgytjekSgOt2BpKcjTlz/HDcbFtWCaisMzWpAnCIIgmKfwWDVrr0eZRFNmlQTd3Fwo4GiTodeLozoLltHCN0Mz1bgEQRAEwyjoaEMpZ1uUrtKwlqCUi22GkyYQiZNFe19BnlIyU5AnCIIgmK/mRZ1QWSn7pqSykvAu4pSpa4ijOgsWr9Hx64UXGOm0Lk2sJRhcMVeGz5YFQRAE83U9KhH/O9EoMTzCxgp8imfHzSVzPQTFu5cFSynIM5VNp8wW5AmCIAjmzc3FDvfc9tgY+W3Axgrcc9tnOmkCkThZvOquDoqfKaewlqCGq4PSYQiCIAgKaljIkY9y2BktebKxgvI57GhYyFEv1xOJk4WzpII8QRAEwfxJkoR3USej7Dyl7DQ1L+r02rSLzBCJUxZgKQV5giAIgmWQJIlGhZ3wKZ4dO2tJ7x/uraXk1jc+xbPTqLD+kiYQxeFZhiUU5AmCIAiWJ16jY9e9GG6+SkIrk6kmmckjvZJPOJoXcTJITa1InLKQ4AcxnHmeYNTkKWWbtFFhsdskCIIgvNujWDUnIuK5HpWEBOlqlqmSkhMuNxdbarg6GLQsRCROWYgsywTei+Hyy0SjJE8pBXn6PFsWBEEQLFu8Rse55wnciEoiIkGLRiejkiRkZGSSd5UkJDSyjMpKwtXemtIutlTObW+Uu7ZF4pTFyLLMvoexBt95StlpaljIUSRNgiAIQobFqHVExGtI0spoZRlrKXlQvKuDCidj9zVAJE5Z1vWoRHbejUGjk/XaINNaSi4Eb1XMSdQ0CYIgCBZHJE5ZmLkV5AmCIAiC0kTiJJhNQZ4gCIIgKE0kTkIqUy/IEwRBEASlicRJeCdTK8gTBEEQBKWJxEkQBEEQBCGNxLaBIAiCIAhCGonESRAEQRAEIY1E4iQIgiAIgpBGInESBEEQBEFII5E4CYIgCIIgpJFInARBEARBENJIJE6CIAiCIAhpJBInQRAEQRCENBKJkyAIgiAIQhqJxEkQBEEQBCGNROIkCIIgCIKQRv8D0Sk5TfwJy/wAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8,8))\n",
-    "\n",
-    "pos = nx.kamada_kawai_layout(filteredTopicsGraph) # k regulates the distance between nodes\n",
-    "\n",
-    "nx.draw(filteredTopicsGraph, with_labels=True, node_color='skyblue', node_size=1500, \n",
-    "        edge_cmap=plt.cm.Blues, pos = pos)\n",
-    "\n",
-    "# plt.show()\n",
-    "# plt.savefig(os.path.join(\".\", \"TopicsCore.png\"), dpi=300, format=\"png\")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "# To be visualized in Gephi\n",
-    "nx.write_gexf(coreDocumentGraph, \"coreDocumentGraph\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Embeddings for the Document-Document Graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing transition probabilities: 100%|██████████| 1050/1050 [00:54<00:00, 19.18it/s]\n",
-      "Generating walks (CPU: 1): 100%|██████████| 10/10 [00:47<00:00,  4.70s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from node2vec import Node2Vec\n",
-    "\n",
-    "node2vec = Node2Vec(coreDocumentGraph, dimensions=20) \n",
-    "model = node2vec.fit(window=10) \n",
-    "embeddings = model.wv "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.manifold import TSNE\n",
-    "\n",
-    "tsne=TSNE(n_components=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedding2d=tsne.fit_transform(embeddings.vectors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a5c64950>]"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(embedding2d[:, 0], embedding2d[:, 1], 'o')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
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-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
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-       "      <th>11</th>\n",
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-       "      <th>15</th>\n",
-       "      <th>16</th>\n",
-       "      <th>17</th>\n",
-       "      <th>18</th>\n",
-       "      <th>19</th>\n",
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-       "  </thead>\n",
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-       "      <td>3.957590</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.162390</td>\n",
-       "      <td>0.251178</td>\n",
-       "      <td>0.186549</td>\n",
-       "      <td>0.988687</td>\n",
-       "      <td>3.733672</td>\n",
-       "      <td>0.626086</td>\n",
-       "      <td>-4.566340</td>\n",
-       "      <td>-2.196690</td>\n",
-       "      <td>-2.987178</td>\n",
-       "      <td>1.805605</td>\n",
-       "      <td>2.033937</td>\n",
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-       "      <td>-0.997341</td>\n",
-       "      <td>0.217447</td>\n",
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-       "      <td>-4.298778</td>\n",
-       "      <td>2.283113</td>\n",
-       "      <td>3.929447</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.560644</td>\n",
-       "      <td>0.339182</td>\n",
-       "      <td>0.291281</td>\n",
-       "      <td>1.226845</td>\n",
-       "      <td>4.163576</td>\n",
-       "      <td>0.812360</td>\n",
-       "      <td>-4.543347</td>\n",
-       "      <td>-2.294925</td>\n",
-       "      <td>-2.825094</td>\n",
-       "      <td>1.498989</td>\n",
-       "      <td>1.761113</td>\n",
-       "      <td>-3.436803</td>\n",
-       "      <td>-4.634498</td>\n",
-       "      <td>-4.171238</td>\n",
-       "      <td>-1.194322</td>\n",
-       "      <td>0.629293</td>\n",
-       "      <td>-1.354635</td>\n",
-       "      <td>-4.453906</td>\n",
-       "      <td>1.763564</td>\n",
-       "      <td>3.575306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-2.838000</td>\n",
-       "      <td>-0.670331</td>\n",
-       "      <td>1.331425</td>\n",
-       "      <td>-2.872283</td>\n",
-       "      <td>0.375439</td>\n",
-       "      <td>4.298521</td>\n",
-       "      <td>-0.158841</td>\n",
-       "      <td>-0.170265</td>\n",
-       "      <td>-2.612220</td>\n",
-       "      <td>0.694892</td>\n",
-       "      <td>3.447092</td>\n",
-       "      <td>-2.284607</td>\n",
-       "      <td>-2.858946</td>\n",
-       "      <td>-4.312414</td>\n",
-       "      <td>-5.989464</td>\n",
-       "      <td>-0.026698</td>\n",
-       "      <td>-1.416963</td>\n",
-       "      <td>-5.212801</td>\n",
-       "      <td>-4.163705</td>\n",
-       "      <td>-0.600494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-2.556705</td>\n",
-       "      <td>0.965237</td>\n",
-       "      <td>-0.061232</td>\n",
-       "      <td>1.352170</td>\n",
-       "      <td>4.276524</td>\n",
-       "      <td>1.577441</td>\n",
-       "      <td>-2.324412</td>\n",
-       "      <td>-0.380508</td>\n",
-       "      <td>-2.863870</td>\n",
-       "      <td>1.455134</td>\n",
-       "      <td>3.609730</td>\n",
-       "      <td>-4.132732</td>\n",
-       "      <td>-2.480354</td>\n",
-       "      <td>-0.950713</td>\n",
-       "      <td>-2.587649</td>\n",
-       "      <td>0.175977</td>\n",
-       "      <td>-1.113148</td>\n",
-       "      <td>-5.748485</td>\n",
-       "      <td>-2.448518</td>\n",
-       "      <td>1.200612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1045</th>\n",
-       "      <td>0.331984</td>\n",
-       "      <td>0.523769</td>\n",
-       "      <td>0.103705</td>\n",
-       "      <td>0.561897</td>\n",
-       "      <td>0.521912</td>\n",
-       "      <td>-0.852221</td>\n",
-       "      <td>-1.032729</td>\n",
-       "      <td>0.337699</td>\n",
-       "      <td>0.679176</td>\n",
-       "      <td>0.848446</td>\n",
-       "      <td>-0.056654</td>\n",
-       "      <td>-0.641469</td>\n",
-       "      <td>-0.082838</td>\n",
-       "      <td>0.306723</td>\n",
-       "      <td>-0.225306</td>\n",
-       "      <td>-0.910874</td>\n",
-       "      <td>-0.478912</td>\n",
-       "      <td>-0.061075</td>\n",
-       "      <td>-0.083310</td>\n",
-       "      <td>-0.278903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1046</th>\n",
-       "      <td>0.166020</td>\n",
-       "      <td>-0.576071</td>\n",
-       "      <td>0.473827</td>\n",
-       "      <td>-0.074774</td>\n",
-       "      <td>0.542515</td>\n",
-       "      <td>-0.566927</td>\n",
-       "      <td>0.635151</td>\n",
-       "      <td>0.547837</td>\n",
-       "      <td>0.188676</td>\n",
-       "      <td>-0.798186</td>\n",
-       "      <td>-0.598822</td>\n",
-       "      <td>0.590872</td>\n",
-       "      <td>-0.403117</td>\n",
-       "      <td>0.617905</td>\n",
-       "      <td>1.012607</td>\n",
-       "      <td>0.195247</td>\n",
-       "      <td>0.337702</td>\n",
-       "      <td>0.182143</td>\n",
-       "      <td>0.600068</td>\n",
-       "      <td>-0.636223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1047</th>\n",
-       "      <td>0.471930</td>\n",
-       "      <td>-0.705889</td>\n",
-       "      <td>0.055459</td>\n",
-       "      <td>-0.975193</td>\n",
-       "      <td>0.099381</td>\n",
-       "      <td>-0.253793</td>\n",
-       "      <td>0.181459</td>\n",
-       "      <td>-0.095714</td>\n",
-       "      <td>0.663958</td>\n",
-       "      <td>-1.006062</td>\n",
-       "      <td>-0.361706</td>\n",
-       "      <td>0.433246</td>\n",
-       "      <td>1.090418</td>\n",
-       "      <td>0.211187</td>\n",
-       "      <td>-0.207774</td>\n",
-       "      <td>-0.705345</td>\n",
-       "      <td>0.371806</td>\n",
-       "      <td>0.108446</td>\n",
-       "      <td>0.908669</td>\n",
-       "      <td>-1.126879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1048</th>\n",
-       "      <td>-0.236891</td>\n",
-       "      <td>0.118807</td>\n",
-       "      <td>-0.029381</td>\n",
-       "      <td>-0.073577</td>\n",
-       "      <td>-0.089825</td>\n",
-       "      <td>0.016194</td>\n",
-       "      <td>0.573447</td>\n",
-       "      <td>-0.264896</td>\n",
-       "      <td>0.540209</td>\n",
-       "      <td>-0.282678</td>\n",
-       "      <td>-0.073139</td>\n",
-       "      <td>0.051319</td>\n",
-       "      <td>0.419923</td>\n",
-       "      <td>0.305243</td>\n",
-       "      <td>-0.105601</td>\n",
-       "      <td>-0.014267</td>\n",
-       "      <td>-0.833588</td>\n",
-       "      <td>0.948402</td>\n",
-       "      <td>0.214101</td>\n",
-       "      <td>0.181394</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1049</th>\n",
-       "      <td>0.303921</td>\n",
-       "      <td>-0.329447</td>\n",
-       "      <td>-0.198187</td>\n",
-       "      <td>-0.968494</td>\n",
-       "      <td>0.568693</td>\n",
-       "      <td>-0.778354</td>\n",
-       "      <td>0.738360</td>\n",
-       "      <td>-0.507104</td>\n",
-       "      <td>0.589299</td>\n",
-       "      <td>-1.071499</td>\n",
-       "      <td>0.007456</td>\n",
-       "      <td>0.132762</td>\n",
-       "      <td>0.568386</td>\n",
-       "      <td>0.526633</td>\n",
-       "      <td>-0.031978</td>\n",
-       "      <td>0.094264</td>\n",
-       "      <td>0.673068</td>\n",
-       "      <td>0.048564</td>\n",
-       "      <td>0.759775</td>\n",
-       "      <td>-1.122763</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1050 rows × 20 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0         1         2         3         4         5         6   \\\n",
-       "0    -0.960548  0.535780  0.385748  1.101508  3.875988  1.070799 -4.849534   \n",
-       "1    -1.162390  0.251178  0.186549  0.988687  3.733672  0.626086 -4.566340   \n",
-       "2    -0.560644  0.339182  0.291281  1.226845  4.163576  0.812360 -4.543347   \n",
-       "3    -2.838000 -0.670331  1.331425 -2.872283  0.375439  4.298521 -0.158841   \n",
-       "4    -2.556705  0.965237 -0.061232  1.352170  4.276524  1.577441 -2.324412   \n",
-       "...        ...       ...       ...       ...       ...       ...       ...   \n",
-       "1045  0.331984  0.523769  0.103705  0.561897  0.521912 -0.852221 -1.032729   \n",
-       "1046  0.166020 -0.576071  0.473827 -0.074774  0.542515 -0.566927  0.635151   \n",
-       "1047  0.471930 -0.705889  0.055459 -0.975193  0.099381 -0.253793  0.181459   \n",
-       "1048 -0.236891  0.118807 -0.029381 -0.073577 -0.089825  0.016194  0.573447   \n",
-       "1049  0.303921 -0.329447 -0.198187 -0.968494  0.568693 -0.778354  0.738360   \n",
-       "\n",
-       "            7         8         9         10        11        12        13  \\\n",
-       "0    -1.820060 -2.966034  1.655846  1.737207 -3.332000 -5.323652 -4.393471   \n",
-       "1    -2.196690 -2.987178  1.805605  2.033937 -3.546881 -4.945525 -4.785186   \n",
-       "2    -2.294925 -2.825094  1.498989  1.761113 -3.436803 -4.634498 -4.171238   \n",
-       "3    -0.170265 -2.612220  0.694892  3.447092 -2.284607 -2.858946 -4.312414   \n",
-       "4    -0.380508 -2.863870  1.455134  3.609730 -4.132732 -2.480354 -0.950713   \n",
-       "...        ...       ...       ...       ...       ...       ...       ...   \n",
-       "1045  0.337699  0.679176  0.848446 -0.056654 -0.641469 -0.082838  0.306723   \n",
-       "1046  0.547837  0.188676 -0.798186 -0.598822  0.590872 -0.403117  0.617905   \n",
-       "1047 -0.095714  0.663958 -1.006062 -0.361706  0.433246  1.090418  0.211187   \n",
-       "1048 -0.264896  0.540209 -0.282678 -0.073139  0.051319  0.419923  0.305243   \n",
-       "1049 -0.507104  0.589299 -1.071499  0.007456  0.132762  0.568386  0.526633   \n",
-       "\n",
-       "            14        15        16        17        18        19  \n",
-       "0    -1.154701  0.506530 -1.585463 -4.248538  2.294325  3.957590  \n",
-       "1    -0.997341  0.217447 -1.068717 -4.298778  2.283113  3.929447  \n",
-       "2    -1.194322  0.629293 -1.354635 -4.453906  1.763564  3.575306  \n",
-       "3    -5.989464 -0.026698 -1.416963 -5.212801 -4.163705 -0.600494  \n",
-       "4    -2.587649  0.175977 -1.113148 -5.748485 -2.448518  1.200612  \n",
-       "...        ...       ...       ...       ...       ...       ...  \n",
-       "1045 -0.225306 -0.910874 -0.478912 -0.061075 -0.083310 -0.278903  \n",
-       "1046  1.012607  0.195247  0.337702  0.182143  0.600068 -0.636223  \n",
-       "1047 -0.207774 -0.705345  0.371806  0.108446  0.908669 -1.126879  \n",
-       "1048 -0.105601 -0.014267 -0.833588  0.948402  0.214101  0.181394  \n",
-       "1049 -0.031978  0.094264  0.673068  0.048564  0.759775 -1.122763  \n",
-       "\n",
-       "[1050 rows x 20 columns]"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(embeddings.vectors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>19</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/20368</th>\n",
-       "      <td>-0.960548</td>\n",
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-       "      <td>3.957590</td>\n",
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-       "    <tr>\n",
-       "      <th>test/20442</th>\n",
-       "      <td>-1.162390</td>\n",
-       "      <td>0.251178</td>\n",
-       "      <td>0.186549</td>\n",
-       "      <td>0.988687</td>\n",
-       "      <td>3.733672</td>\n",
-       "      <td>0.626086</td>\n",
-       "      <td>-4.566340</td>\n",
-       "      <td>-2.196690</td>\n",
-       "      <td>-2.987178</td>\n",
-       "      <td>1.805605</td>\n",
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-       "      <td>3.929447</td>\n",
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-       "    <tr>\n",
-       "      <th>test/20221</th>\n",
-       "      <td>-0.560644</td>\n",
-       "      <td>0.339182</td>\n",
-       "      <td>0.291281</td>\n",
-       "      <td>1.226845</td>\n",
-       "      <td>4.163576</td>\n",
-       "      <td>0.812360</td>\n",
-       "      <td>-4.543347</td>\n",
-       "      <td>-2.294925</td>\n",
-       "      <td>-2.825094</td>\n",
-       "      <td>1.498989</td>\n",
-       "      <td>1.761113</td>\n",
-       "      <td>-3.436803</td>\n",
-       "      <td>-4.634498</td>\n",
-       "      <td>-4.171238</td>\n",
-       "      <td>-1.194322</td>\n",
-       "      <td>0.629293</td>\n",
-       "      <td>-1.354635</td>\n",
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-       "      <td>1.763564</td>\n",
-       "      <td>3.575306</td>\n",
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-       "    <tr>\n",
-       "      <th>test/16715</th>\n",
-       "      <td>-2.838000</td>\n",
-       "      <td>-0.670331</td>\n",
-       "      <td>1.331425</td>\n",
-       "      <td>-2.872283</td>\n",
-       "      <td>0.375439</td>\n",
-       "      <td>4.298521</td>\n",
-       "      <td>-0.158841</td>\n",
-       "      <td>-0.170265</td>\n",
-       "      <td>-2.612220</td>\n",
-       "      <td>0.694892</td>\n",
-       "      <td>3.447092</td>\n",
-       "      <td>-2.284607</td>\n",
-       "      <td>-2.858946</td>\n",
-       "      <td>-4.312414</td>\n",
-       "      <td>-5.989464</td>\n",
-       "      <td>-0.026698</td>\n",
-       "      <td>-1.416963</td>\n",
-       "      <td>-5.212801</td>\n",
-       "      <td>-4.163705</td>\n",
-       "      <td>-0.600494</td>\n",
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-       "    <tr>\n",
-       "      <th>test/20800</th>\n",
-       "      <td>-2.556705</td>\n",
-       "      <td>0.965237</td>\n",
-       "      <td>-0.061232</td>\n",
-       "      <td>1.352170</td>\n",
-       "      <td>4.276524</td>\n",
-       "      <td>1.577441</td>\n",
-       "      <td>-2.324412</td>\n",
-       "      <td>-0.380508</td>\n",
-       "      <td>-2.863870</td>\n",
-       "      <td>1.455134</td>\n",
-       "      <td>3.609730</td>\n",
-       "      <td>-4.132732</td>\n",
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-       "      <td>-0.950713</td>\n",
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-       "      <td>0.175977</td>\n",
-       "      <td>-1.113148</td>\n",
-       "      <td>-5.748485</td>\n",
-       "      <td>-2.448518</td>\n",
-       "      <td>1.200612</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>test/20255</th>\n",
-       "      <td>0.331984</td>\n",
-       "      <td>0.523769</td>\n",
-       "      <td>0.103705</td>\n",
-       "      <td>0.561897</td>\n",
-       "      <td>0.521912</td>\n",
-       "      <td>-0.852221</td>\n",
-       "      <td>-1.032729</td>\n",
-       "      <td>0.337699</td>\n",
-       "      <td>0.679176</td>\n",
-       "      <td>0.848446</td>\n",
-       "      <td>-0.056654</td>\n",
-       "      <td>-0.641469</td>\n",
-       "      <td>-0.082838</td>\n",
-       "      <td>0.306723</td>\n",
-       "      <td>-0.225306</td>\n",
-       "      <td>-0.910874</td>\n",
-       "      <td>-0.478912</td>\n",
-       "      <td>-0.061075</td>\n",
-       "      <td>-0.083310</td>\n",
-       "      <td>-0.278903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/20266</th>\n",
-       "      <td>0.166020</td>\n",
-       "      <td>-0.576071</td>\n",
-       "      <td>0.473827</td>\n",
-       "      <td>-0.074774</td>\n",
-       "      <td>0.542515</td>\n",
-       "      <td>-0.566927</td>\n",
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-       "      <td>0.547837</td>\n",
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-       "      <td>-0.598822</td>\n",
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-       "      <td>-0.403117</td>\n",
-       "      <td>0.617905</td>\n",
-       "      <td>1.012607</td>\n",
-       "      <td>0.195247</td>\n",
-       "      <td>0.337702</td>\n",
-       "      <td>0.182143</td>\n",
-       "      <td>0.600068</td>\n",
-       "      <td>-0.636223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/10885</th>\n",
-       "      <td>0.471930</td>\n",
-       "      <td>-0.705889</td>\n",
-       "      <td>0.055459</td>\n",
-       "      <td>-0.975193</td>\n",
-       "      <td>0.099381</td>\n",
-       "      <td>-0.253793</td>\n",
-       "      <td>0.181459</td>\n",
-       "      <td>-0.095714</td>\n",
-       "      <td>0.663958</td>\n",
-       "      <td>-1.006062</td>\n",
-       "      <td>-0.361706</td>\n",
-       "      <td>0.433246</td>\n",
-       "      <td>1.090418</td>\n",
-       "      <td>0.211187</td>\n",
-       "      <td>-0.207774</td>\n",
-       "      <td>-0.705345</td>\n",
-       "      <td>0.371806</td>\n",
-       "      <td>0.108446</td>\n",
-       "      <td>0.908669</td>\n",
-       "      <td>-1.126879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/11154</th>\n",
-       "      <td>-0.236891</td>\n",
-       "      <td>0.118807</td>\n",
-       "      <td>-0.029381</td>\n",
-       "      <td>-0.073577</td>\n",
-       "      <td>-0.089825</td>\n",
-       "      <td>0.016194</td>\n",
-       "      <td>0.573447</td>\n",
-       "      <td>-0.264896</td>\n",
-       "      <td>0.540209</td>\n",
-       "      <td>-0.282678</td>\n",
-       "      <td>-0.073139</td>\n",
-       "      <td>0.051319</td>\n",
-       "      <td>0.419923</td>\n",
-       "      <td>0.305243</td>\n",
-       "      <td>-0.105601</td>\n",
-       "      <td>-0.014267</td>\n",
-       "      <td>-0.833588</td>\n",
-       "      <td>0.948402</td>\n",
-       "      <td>0.214101</td>\n",
-       "      <td>0.181394</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/1532</th>\n",
-       "      <td>0.303921</td>\n",
-       "      <td>-0.329447</td>\n",
-       "      <td>-0.198187</td>\n",
-       "      <td>-0.968494</td>\n",
-       "      <td>0.568693</td>\n",
-       "      <td>-0.778354</td>\n",
-       "      <td>0.738360</td>\n",
-       "      <td>-0.507104</td>\n",
-       "      <td>0.589299</td>\n",
-       "      <td>-1.071499</td>\n",
-       "      <td>0.007456</td>\n",
-       "      <td>0.132762</td>\n",
-       "      <td>0.568386</td>\n",
-       "      <td>0.526633</td>\n",
-       "      <td>-0.031978</td>\n",
-       "      <td>0.094264</td>\n",
-       "      <td>0.673068</td>\n",
-       "      <td>0.048564</td>\n",
-       "      <td>0.759775</td>\n",
-       "      <td>-1.122763</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1050 rows × 20 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      0         1         2         3         4         5   \\\n",
-       "test/20368     -0.960548  0.535780  0.385748  1.101508  3.875988  1.070799   \n",
-       "test/20442     -1.162390  0.251178  0.186549  0.988687  3.733672  0.626086   \n",
-       "test/20221     -0.560644  0.339182  0.291281  1.226845  4.163576  0.812360   \n",
-       "test/16715     -2.838000 -0.670331  1.331425 -2.872283  0.375439  4.298521   \n",
-       "test/20800     -2.556705  0.965237 -0.061232  1.352170  4.276524  1.577441   \n",
-       "...                  ...       ...       ...       ...       ...       ...   \n",
-       "test/20255      0.331984  0.523769  0.103705  0.561897  0.521912 -0.852221   \n",
-       "test/20266      0.166020 -0.576071  0.473827 -0.074774  0.542515 -0.566927   \n",
-       "training/10885  0.471930 -0.705889  0.055459 -0.975193  0.099381 -0.253793   \n",
-       "training/11154 -0.236891  0.118807 -0.029381 -0.073577 -0.089825  0.016194   \n",
-       "training/1532   0.303921 -0.329447 -0.198187 -0.968494  0.568693 -0.778354   \n",
-       "\n",
-       "                      6         7         8         9         10        11  \\\n",
-       "test/20368     -4.849534 -1.820060 -2.966034  1.655846  1.737207 -3.332000   \n",
-       "test/20442     -4.566340 -2.196690 -2.987178  1.805605  2.033937 -3.546881   \n",
-       "test/20221     -4.543347 -2.294925 -2.825094  1.498989  1.761113 -3.436803   \n",
-       "test/16715     -0.158841 -0.170265 -2.612220  0.694892  3.447092 -2.284607   \n",
-       "test/20800     -2.324412 -0.380508 -2.863870  1.455134  3.609730 -4.132732   \n",
-       "...                  ...       ...       ...       ...       ...       ...   \n",
-       "test/20255     -1.032729  0.337699  0.679176  0.848446 -0.056654 -0.641469   \n",
-       "test/20266      0.635151  0.547837  0.188676 -0.798186 -0.598822  0.590872   \n",
-       "training/10885  0.181459 -0.095714  0.663958 -1.006062 -0.361706  0.433246   \n",
-       "training/11154  0.573447 -0.264896  0.540209 -0.282678 -0.073139  0.051319   \n",
-       "training/1532   0.738360 -0.507104  0.589299 -1.071499  0.007456  0.132762   \n",
-       "\n",
-       "                      12        13        14        15        16        17  \\\n",
-       "test/20368     -5.323652 -4.393471 -1.154701  0.506530 -1.585463 -4.248538   \n",
-       "test/20442     -4.945525 -4.785186 -0.997341  0.217447 -1.068717 -4.298778   \n",
-       "test/20221     -4.634498 -4.171238 -1.194322  0.629293 -1.354635 -4.453906   \n",
-       "test/16715     -2.858946 -4.312414 -5.989464 -0.026698 -1.416963 -5.212801   \n",
-       "test/20800     -2.480354 -0.950713 -2.587649  0.175977 -1.113148 -5.748485   \n",
-       "...                  ...       ...       ...       ...       ...       ...   \n",
-       "test/20255     -0.082838  0.306723 -0.225306 -0.910874 -0.478912 -0.061075   \n",
-       "test/20266     -0.403117  0.617905  1.012607  0.195247  0.337702  0.182143   \n",
-       "training/10885  1.090418  0.211187 -0.207774 -0.705345  0.371806  0.108446   \n",
-       "training/11154  0.419923  0.305243 -0.105601 -0.014267 -0.833588  0.948402   \n",
-       "training/1532   0.568386  0.526633 -0.031978  0.094264  0.673068  0.048564   \n",
-       "\n",
-       "                      18        19  \n",
-       "test/20368      2.294325  3.957590  \n",
-       "test/20442      2.283113  3.929447  \n",
-       "test/20221      1.763564  3.575306  \n",
-       "test/16715     -4.163705 -0.600494  \n",
-       "test/20800     -2.448518  1.200612  \n",
-       "...                  ...       ...  \n",
-       "test/20255     -0.083310 -0.278903  \n",
-       "test/20266      0.600068 -0.636223  \n",
-       "training/10885  0.908669 -1.126879  \n",
-       "training/11154  0.214101  0.181394  \n",
-       "training/1532   0.759775 -1.122763  \n",
-       "\n",
-       "[1050 rows x 20 columns]"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(embeddings.vectors, index=embeddings.index2word)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: \n",
-      "Type: Graph\n",
-      "Number of nodes: 25752\n",
-      "Number of edges: 100311\n",
-      "Average degree:   7.7905\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nx.info(G))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extracting the embeddings to be used in shallow-learning models. Please do vary the *dimensions* and the *window* parameters to generate multiple combination to be cross-validated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "Path(\"./embeddings\").mkdir(parents=True, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing transition probabilities: 100%|██████████| 25752/25752 [03:59<00:00, 107.40it/s] \n",
-      "Generating walks (CPU: 1): 100%|██████████| 10/10 [34:19<00:00, 205.97s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from node2vec import Node2Vec\n",
-    "\n",
-    "dimensions = 10\n",
-    "window = 20\n",
-    "\n",
-    "node2vec = Node2Vec(G, dimensions=dimensions) \n",
-    "model = node2vec.fit(window=window) \n",
-    "embeddings = model.wv \n",
-    "\n",
-    "pd.DataFrame(embeddings.vectors, index=embeddings.index2word)\\\n",
-    "    .to_pickle(f\"./embeddings/bipartiteGraphEmbeddings_{dimensions}_{window}.p\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ml-book-7",
-   "language": "python",
-   "name": "ml-book-7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter07/02_Social_network_analysis.ipynb b/Chapter07/02_Social_network_analysis.ipynb
new file mode 100644
index 0000000..af2bb87
--- /dev/null
+++ b/Chapter07/02_Social_network_analysis.ipynb
@@ -0,0 +1,782 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "G-iq6EXuNk18"
+   },
+   "source": [
+    "# Link prediction on social network using DGL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XK8GxEU285LJ"
+   },
+   "outputs": [],
+   "source": [
+    "# import the social network graph\n",
+    "import pickle\n",
+    "with open('test.gpickle', 'rb') as f:\n",
+    "  Gnx = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2BFI_Tus_9Rv"
+   },
+   "outputs": [],
+   "source": [
+    "import dgl\n",
+    "\n",
+    "# convert the graph from networkx to dgl. We are now ready to start learning\n",
+    "G = dgl.from_networkx(Gnx)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u88BLEDkJpJd"
+   },
+   "source": [
+    "In the code above, we are implementing a GraphSAGE model to perform link prediction on a graph using the Deep Graph Library (DGL) and PyTorch. We start by setting up the computational device and initializing the node features as identity matrices. The graph's edges are then split into training and testing sets to evaluate the model's performance on unseen data. Negative edges are sampled to serve as negative examples during training. We define a GraphSAGE model with two layers that aggregate neighbor information and a dot-product-based edge predictor to compute edge scores. The model is trained using binary cross-entropy loss, optimized with the Adam optimizer. After training for a specified number of epochs, we evaluate the model's performance using common metrics on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xO0n7SsKKDUP"
+   },
+   "outputs": [],
+   "source": [
+    "import dgl\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from dgl.nn import SAGEConv\n",
+    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
+    "import numpy as np\n",
+    "import scipy.sparse as sp\n",
+    "from torch import nn\n",
+    "import itertools\n",
+    "import dgl.function as fn\n",
+    "\n",
+    "# Set the computation device to GPU if available, otherwise CPU\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "\n",
+    "# Assuming graph G is pre-defined and moving it to the computation device\n",
+    "graph = G.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xhgraf86KGMz"
+   },
+   "source": [
+    "Once the graph is loaded, we need to perform the following steps:\n",
+    "- assign the fake features (i.e. the identity matrix)\n",
+    "- splitting edges into training edges (90%) and test edges (10%)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "T3lgwzSHD9Ka"
+   },
+   "outputs": [],
+   "source": [
+    "# Assigning a unique identity feature to each node\n",
+    "# This helps the model to have initial distinguishable features for each node\n",
+    "node_features = torch.eye(graph.number_of_nodes()).to(device)\n",
+    "graph.ndata['features'] = node_features\n",
+    "\n",
+    "# Splitting edges into training and test sets\n",
+    "# This helps in evaluating the model performance on unseen data\n",
+    "src_nodes, dst_nodes = graph.edges()\n",
+    "edge_ids = np.arange(graph.number_of_edges())\n",
+    "np.random.shuffle(edge_ids)\n",
+    "\n",
+    "# Define the number of test edges (10% of total edges)\n",
+    "test_edge_count = int(0.1 * len(edge_ids))\n",
+    "train_edge_count = len(edge_ids) - test_edge_count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Jce-FNxeKqcn"
+   },
+   "source": [
+    "Next, we need to find negative (i.e. non existent) edges. This because we may want to train the model whether an edge exists.. or not!\n",
+    "We will be doing this by defining an adjacency matrix and randomly picking negative edges.\n",
+    "\n",
+    "Finally, we create a test graph for model evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_1_fmldeKo86"
+   },
+   "outputs": [],
+   "source": [
+    "# Splitting edges into positive training and testing sets\n",
+    "# Positive edges simulate the real edges in the graph\n",
+    "test_pos_src, test_pos_dst = src_nodes[edge_ids[:test_edge_count]], dst_nodes[edge_ids[:test_edge_count]]\n",
+    "train_pos_src, train_pos_dst = src_nodes[edge_ids[test_edge_count:]], dst_nodes[edge_ids[test_edge_count:]]\n",
+    "\n",
+    "# Creating an adjacency matrix and finding negative edges\n",
+    "# Negative edges are non-existent edges in the graph used for negative sampling\n",
+    "adj_matrix = sp.coo_matrix((np.ones(len(src_nodes)), (src_nodes.numpy(), dst_nodes.numpy())), shape=(graph.number_of_nodes(), graph.number_of_nodes()))\n",
+    "neg_adj_matrix = 1 - adj_matrix.toarray() - np.eye(graph.number_of_nodes())\n",
+    "neg_src, neg_dst = np.where(neg_adj_matrix != 0)\n",
+    "neg_edge_ids = np.random.choice(len(neg_src), size=graph.number_of_edges(), replace=False)\n",
+    "\n",
+    "# Splitting negative edges into training and testing sets\n",
+    "# These edges serve as negative samples during training and testing\n",
+    "test_neg_src, test_neg_dst = neg_src[neg_edge_ids[:test_edge_count]], neg_dst[neg_edge_ids[:test_edge_count]]\n",
+    "train_neg_src, train_neg_dst = neg_src[neg_edge_ids[test_edge_count:]], neg_dst[neg_edge_ids[test_edge_count:]]\n",
+    "\n",
+    "# Creating a training graph by removing test edges\n",
+    "# This prevents the model from training on test data and helps evaluate its generalization capability\n",
+    "train_graph = dgl.remove_edges(graph, edge_ids[:test_edge_count])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GPAANUqWLX5z"
+   },
+   "source": [
+    "We are now ready to train the model.\n",
+    "The next steps are the followings:-\n",
+    "- create a GNN model (we choose a GraphSAGE model in this case)\n",
+    "- attach an edge predictor (in this case we choose to compute the \"existence\" score for an edge by taking the dot product of the embeddings of the two end nodes\n",
+    "- implement the train loop which computes the predictions, the loss value, and applies backpropagate to update the model weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XV_OfWU3LWJx",
+    "outputId": "7a68eda0-e744-4f74-8317-3dc06e351b2e"
+   },
+   "outputs": [],
+   "source": [
+    "# Building the GraphSAGE model\n",
+    "# This model consists of two GraphSAGE layers, each computes new node representations by averaging neighbor information\n",
+    "# DGL provides dgl.nn.SAGEConv that conveniently creates a GraphSAGE layer\n",
+    "class GraphSAGENetwork(nn.Module):\n",
+    "    def __init__(self, in_feats, hidden_feats):\n",
+    "        super(GraphSAGENetwork, self).__init__()\n",
+    "        self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean')\n",
+    "        self.conv2 = SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')\n",
+    "\n",
+    "    def forward(self, g, features):\n",
+    "        h = self.conv1(g, features)\n",
+    "        h = F.relu(h)\n",
+    "        h = self.conv2(g, h)\n",
+    "        return h\n",
+    "\n",
+    "# Defining the edge predictor using dot product\n",
+    "# This predictor computes the score for an edge by taking the dot product of the embeddings of the two end nodes\n",
+    "class DotProductPredictor(nn.Module):\n",
+    "    def forward(self, graph, node_embeddings):\n",
+    "        with graph.local_scope():\n",
+    "            graph.ndata['h'] = node_embeddings\n",
+    "            graph.apply_edges(dgl.function.u_dot_v('h', 'h', 'score'))\n",
+    "            return graph.edata['score'][:, 0]\n",
+    "\n",
+    "# Initialize the GraphSAGE model and the predictor\n",
+    "sage_model = GraphSAGENetwork(graph.ndata['features'].shape[1], 16).to(device)\n",
+    "predictor = DotProductPredictor().to(device)\n",
+    "\n",
+    "# Function to compute the loss\n",
+    "# This combines the positive and negative scores and uses binary cross-entropy loss to measure performance\n",
+    "def compute_loss(pos_scores, neg_scores):\n",
+    "    scores = torch.cat([pos_scores, neg_scores])\n",
+    "    labels = torch.cat([torch.ones_like(pos_scores), torch.zeros_like(neg_scores)])\n",
+    "    return F.binary_cross_entropy_with_logits(scores, labels)\n",
+    "\n",
+    "# Optimizer setup\n",
+    "# Using Adam optimizer to update model parameters based on the gradients computed during backpropagation\n",
+    "optimizer = torch.optim.Adam(itertools.chain(sage_model.parameters(), predictor.parameters()), lr=0.01)\n",
+    "\n",
+    "# Training loop\n",
+    "# The model is trained for a specified number of epochs\n",
+    "for epoch in range(100):\n",
+    "    sage_model.train()\n",
+    "\n",
+    "    # Compute node embeddings\n",
+    "    node_embeddings = sage_model(train_graph, train_graph.ndata['features'])\n",
+    "\n",
+    "    # Compute scores for positive and negative edges\n",
+    "    pos_scores = predictor(dgl.graph((train_pos_src, train_pos_dst), num_nodes=graph.number_of_nodes()).to(device), node_embeddings)\n",
+    "    neg_scores = predictor(dgl.graph((train_neg_src, train_neg_dst), num_nodes=graph.number_of_nodes()).to(device), node_embeddings)\n",
+    "\n",
+    "    # Compute loss\n",
+    "    loss = compute_loss(pos_scores, neg_scores)\n",
+    "\n",
+    "    # Backward pass: compute gradients and update model parameters\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    # Print loss every 5 epochs\n",
+    "    if epoch % 5 == 0:\n",
+    "        print(f'Epoch {epoch}, Loss: {loss.item()}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CfpuLRMBMKq5"
+   },
+   "source": [
+    "Let's evaluate the model by means of f1-score, precision and recall."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qkEwfrhPJUzW",
+    "outputId": "89ce9634-98b0-433c-ecec-2c944de83a97"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def normalize(scores):\n",
+    "  return (scores - scores.min()) / (scores.max() - scores.min())\n",
+    "\n",
+    "# Define the score computation to evaluate model performance on classification tasks\n",
+    "def compute_scores(positive_scores, negative_scores):\n",
+    "    scores = torch.cat([positive_scores, negative_scores]).numpy()\n",
+    "    labels = torch.cat([torch.ones(positive_scores.shape[0]), torch.zeros(negative_scores.shape[0])]).numpy()\n",
+    "    return (f1_score(labels, scores),\n",
+    "            precision_score(labels, scores),\n",
+    "            recall_score(labels, scores))\n",
+    "\n",
+    "test_pos_graph = dgl.graph((test_pos_src, test_pos_dst), num_nodes=graph.number_of_nodes()).to(device)\n",
+    "test_neg_graph = dgl.graph((test_neg_src, test_neg_dst), num_nodes=graph.number_of_nodes()).to(device)\n",
+    "test_node_embeddings = sage_model(graph, graph.ndata['features'])\n",
+    "\n",
+    "# Evaluate model performance using proper metrics\n",
+    "with torch.no_grad():\n",
+    "    test_pos_scores = predictor(test_pos_graph, test_node_embeddings)\n",
+    "    test_neg_scores = predictor(test_neg_graph, test_node_embeddings)\n",
+    "\n",
+    "    pos_test_scores = predictor(test_pos_graph, node_embeddings)\n",
+    "    neg_test_scores = predictor(test_neg_graph, node_embeddings)\n",
+    "\n",
+    "    pos_test_scores = (normalize(pos_test_scores) > 0.5) * 1\n",
+    "    neg_test_scores = (normalize(neg_test_scores) > 0.5) * 1\n",
+    "\n",
+    "    f1, prec, rec = compute_scores(pos_test_scores, neg_test_scores)\n",
+    "    print(f'F1 Score: {f1}')\n",
+    "    print(f'Precision: {prec}')\n",
+    "    print(f'Recall: {rec}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kQVSkOiHNCv2"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pSeHwaf0In_Z"
+   },
+   "source": [
+    "## Dealing with large graphs\n",
+    "In the previous example we have seen how to predict link using DGL. However, you may have noticed that we computed the probability of all edges at once during training, which, in case of large graphs, is not feasible.\n",
+    "\n",
+    "To overcome this issue, we can use some functionalities provided by graph machine learning libraries, including DGL. In the next example, instead of fitting the whole graph in memory, we will be iterating over the edges in minibatches.\n",
+    "\n",
+    "For readability we are not going to implement the validation and testing part, however it can be done as we have done above!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "H_fLo8j8InGq"
+   },
+   "outputs": [],
+   "source": [
+    "# DGL provides dgl.dataloading.EdgeDataLoader to iterate over edges for edge classification or link prediction tasks.\n",
+    "# For link prediction, we also need to specify a negative sampler\n",
+    "# builtin negative samplers ( non-existing edges) such as dgl.dataloading.negative_sampler.Uniform can be used for this purpose.\n",
+    "\n",
+    "# load 5 negative sample per each positive sample (existing edges)\n",
+    "negative_sampler = dgl.dataloading.negative_sampler.Uniform(5)\n",
+    "\n",
+    "# define the edge loader\n",
+    "sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)\n",
+    "sampler = dgl.dataloading.as_edge_prediction_sampler(\n",
+    "    sampler, negative_sampler=negative_sampler)\n",
+    "\n",
+    "dataloader = dgl.dataloading.DataLoader(\n",
+    "    # The following arguments are specific to NodeDataLoader.\n",
+    "    graph,                                      # The graph\n",
+    "    torch.arange(graph.number_of_edges()),  # The edges to iterate over\n",
+    "    sampler,                                # The neighbor sampler\n",
+    "    device=device,                          # Put the MFGs on CPU or GPU\n",
+    "    # The following arguments are inherited from PyTorch DataLoader.\n",
+    "    batch_size=128,    # Batch size\n",
+    "    shuffle=True,       # Whether to shuffle the nodes for every epoch\n",
+    "    drop_last=False,    # Whether to drop the last incomplete batch\n",
+    "    num_workers=0       # Number of sampler processes\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cgzvBz4sInOq",
+    "outputId": "812c2a79-7f6c-4565-88ac-cdb55380b0fa"
+   },
+   "outputs": [],
+   "source": [
+    "input_nodes, pos_graph, neg_graph, mfgs = next(iter(dataloader))\n",
+    "print('Number of input nodes:', len(input_nodes))\n",
+    "print('Positive graph # nodes:', pos_graph.number_of_nodes(), '# edges:', pos_graph.number_of_edges())\n",
+    "print('Negative graph # nodes:', neg_graph.number_of_nodes(), '# edges:', neg_graph.number_of_edges())\n",
+    "\n",
+    "print(mfgs)\n",
+    "# Notice that the last element is a list of message flow graphs (MFGs) storing the computation dependencies for each GNN layer.\n",
+    "# The MFGs are used to compute the GNN outputs of the nodes involved in positive/negative graph.\n",
+    "# Check more on https://docs.dgl.ai/en/0.8.x/generated/dgl.dataloading.BlockSampler.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jg23gAE7MopC",
+    "outputId": "3c132fb5-cef4-4361-cb96-3e13580994df"
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from dgl.nn import SAGEConv\n",
+    "\n",
+    "class GraphSAGENetwork(nn.Module):\n",
+    "    def __init__(self, in_feats, hidden_feats):\n",
+    "        super(GraphSAGENetwork, self).__init__()\n",
+    "        self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean')\n",
+    "        self.conv2 = SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')\n",
+    "\n",
+    "    def forward(self, g, features):\n",
+    "        h = self.conv1(g[0], features)\n",
+    "        h = F.relu(h)\n",
+    "        h = self.conv2(g[1], h)\n",
+    "        return h\n",
+    "\n",
+    "# Defining the edge predictor using dot product\n",
+    "# This predictor computes the score for an edge by taking the dot product of the embeddings of the two end nodes\n",
+    "class DotProductPredictor(nn.Module):\n",
+    "    def forward(self, graph, node_embeddings):\n",
+    "        with graph.local_scope():\n",
+    "            graph.ndata['h'] = node_embeddings\n",
+    "            graph.apply_edges(dgl.function.u_dot_v('h', 'h', 'score'))\n",
+    "            return graph.edata['score'][:, 0]\n",
+    "\n",
+    "# Initialize the GraphSAGE model and the predictor\n",
+    "sage_model = GraphSAGENetwork(graph.ndata['features'].shape[1], 16).to(device)\n",
+    "predictor = DotProductPredictor().to(device)\n",
+    "\n",
+    "# Optimizer setup\n",
+    "# Using Adam optimizer to update model parameters based on the gradients computed during backpropagation\n",
+    "optimizer = torch.optim.Adam(itertools.chain(sage_model.parameters(), predictor.parameters()), lr=0.01)\n",
+    "\n",
+    "# Training loop\n",
+    "# The model is trained for a specified number of epochs\n",
+    "for epoch in range(5):\n",
+    "  total_loss = total_examples = 0\n",
+    "  for (input_nodes, pos_graph, neg_graph, mfgs) in dataloader:\n",
+    "    sage_model.train()\n",
+    "\n",
+    "    input_features = mfgs[0].srcdata['features']\n",
+    "\n",
+    "    # Compute node embeddings\n",
+    "    node_embeddings = sage_model(mfgs, input_features)\n",
+    "\n",
+    "    # Compute scores for positive and negative edges\n",
+    "    pos_scores = predictor(pos_graph, node_embeddings)\n",
+    "    neg_scores = predictor(neg_graph, node_embeddings)\n",
+    "\n",
+    "    # Compute loss\n",
+    "    loss = compute_loss(pos_scores, neg_scores)\n",
+    "\n",
+    "    # Backward pass: compute gradients and update model parameters\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    total_loss += float(loss) * (len(pos_scores) + len(neg_scores))\n",
+    "    total_examples += (len(pos_scores) + len(neg_scores))\n",
+    "\n",
+    "  print(f\"Epoch: {epoch:03d}, Loss: {total_loss / total_examples:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "72eyir3cInRH"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5cPGcLx3NtA8"
+   },
+   "source": [
+    "# Link prediction on social network using PyG\n",
+    "We will now replicate the example using another popular library for graph machine learning: Pytorch Geometric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UuygURxWN-jf",
+    "outputId": "ec9e85a6-97b1-4569-cafe-d74115ef13a6"
+   },
+   "outputs": [],
+   "source": [
+    "from torch_geometric.utils.convert import from_networkx\n",
+    "import torch_geometric.transforms as T\n",
+    "from torch_geometric.loader import LinkNeighborLoader\n",
+    "from torch_geometric.nn import SAGEConv\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# Convert the graph into PyTorch geometric\n",
+    "G = from_networkx(Gnx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AGEAdLxMO8-w"
+   },
+   "outputs": [],
+   "source": [
+    "# let's add fake features\n",
+    "G.x = torch.eye(G.num_nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pBliMBhdOr1C"
+   },
+   "outputs": [],
+   "source": [
+    "# we first split the set of edges into training (80%), validation (10%),\n",
+    "# and testing edges (10%). We also generate fixed negative (non existing)\n",
+    "# edges for evaluation with a ratio of 2:1.\n",
+    "# We can leverage the `RandomLinkSplit()` transform to perform all the steps:\n",
+    "transform = T.RandomLinkSplit(\n",
+    "    num_val=0.1,\n",
+    "    num_test=0.1,\n",
+    "    disjoint_train_ratio=0.3,\n",
+    "    neg_sampling_ratio=2.0,\n",
+    "    add_negative_train_samples=False\n",
+    ")\n",
+    "train_data, val_data, test_data = transform(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "stNh0Op1P9nG"
+   },
+   "source": [
+    "Similar to what we have done above, we will be using a mini-batch loader: our graph is quite small, so it is perfectly fine to load it in memory while training. However, for larger graphs, since computing the probability of all edges is usually not feasible, a mini-batch loader is required to load parts of the graph step by step.\n",
+    "\n",
+    "PyG makes use of the loader.LinkNeighborLoader to sample multiple hops from both ends of a link and creates a subgraph from it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wy3Gvd0bPm5f"
+   },
+   "outputs": [],
+   "source": [
+    "# Define seed edges:\n",
+    "edge_label_index = train_data.edge_label_index\n",
+    "edge_label = train_data.edge_label\n",
+    "train_loader = LinkNeighborLoader(\n",
+    "    data=train_data,\n",
+    "    num_neighbors=[20, 20],\n",
+    "    neg_sampling_ratio=2.0,\n",
+    "    edge_label_index=edge_label_index,\n",
+    "    edge_label=edge_label,\n",
+    "    batch_size=128,\n",
+    "    shuffle=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zk2gPxT0QGhq"
+   },
+   "outputs": [],
+   "source": [
+    "# Building the GraphSAGE model\n",
+    "# This model consists of two GraphSAGE layers, each computes new node representations by averaging neighbor information\n",
+    "class GraphSAGENetwork(nn.Module):\n",
+    "    def __init__(self, in_feats, hidden_feats):\n",
+    "        super(GraphSAGENetwork, self).__init__()\n",
+    "        self.conv1 = SAGEConv(in_feats, hidden_feats)\n",
+    "        self.conv2 = SAGEConv(hidden_feats, hidden_feats)\n",
+    "\n",
+    "    def forward(self, x, edge_index):\n",
+    "        h = self.conv1(x, edge_index)\n",
+    "        h = F.relu(h)\n",
+    "        h = self.conv2(h, edge_index)\n",
+    "        return h\n",
+    "\n",
+    "# Defining the edge predictor using dot product\n",
+    "# This predictor computes the score for an edge by taking the dot product of the embeddings of the two end nodes\n",
+    "class DotProductPredictor(nn.Module):\n",
+    "    def forward(self, z, edge_index):\n",
+    "        src, dst = edge_index\n",
+    "        return (z[src] * z[dst]).sum(dim=-1)\n",
+    "\n",
+    "# Initialize the GraphSAGE model and the predictor\n",
+    "sage_model = GraphSAGENetwork(G.num_features, 16).to(device)\n",
+    "predictor = DotProductPredictor().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZDjttEQvQnn3"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to compute the loss\n",
+    "# This combines the positive and negative scores and uses binary cross-entropy loss to measure performance\n",
+    "def compute_loss(pred, ground_truth):\n",
+    "    loss = F.binary_cross_entropy_with_logits(pred, ground_truth)\n",
+    "    return loss\n",
+    "\n",
+    "# Function to compute the prediction score\n",
+    "def compute_scores(labels, scores):\n",
+    "    return (f1_score(labels, scores),\n",
+    "            precision_score(labels, scores),\n",
+    "            recall_score(labels, scores))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "acSDECu0Q_Ja",
+    "outputId": "68eceb76-9708-42c4-be1d-8454df84e356"
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Optimizer setup\n",
+    "# Using Adam optimizer to update model parameters based on the gradients computed during backpropagation\n",
+    "optimizer = torch.optim.Adam(itertools.chain(sage_model.parameters(), predictor.parameters()), lr=0.01)\n",
+    "\n",
+    "# Training loop\n",
+    "# The model is trained for a specified number of epochs\n",
+    "for epoch in range(1):\n",
+    "    sage_model.train()\n",
+    "    total_loss = total_examples = 0\n",
+    "\n",
+    "    for batch in tqdm(train_loader):\n",
+    "      optimizer.zero_grad()\n",
+    "      batch.to(device)\n",
+    "\n",
+    "      # Compute node embeddings\n",
+    "      node_embeddings = sage_model(batch.x, batch.edge_index)\n",
+    "      scores = predictor(node_embeddings, batch.edge_label_index)\n",
+    "\n",
+    "      # Compute loss\n",
+    "      loss = compute_loss(scores, batch.edge_label)\n",
+    "\n",
+    "      # Backward pass: compute gradients and update model parameters\n",
+    "      loss.backward()\n",
+    "      optimizer.step()\n",
+    "      total_loss += float(loss) * scores.numel()\n",
+    "      total_examples += scores.numel()\n",
+    "\n",
+    "    print(f\"Epoch: {epoch:03d}, Loss: {total_loss / total_examples:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_bc8WNOFVd_6"
+   },
+   "source": [
+    "Let's evaluate the model. For doing this we will be creating a proper linkneighborloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "20mj7rUxQpD0",
+    "outputId": "635a3bd2-983c-46e9-b8af-7701fe312e09"
+   },
+   "outputs": [],
+   "source": [
+    "# Define the validation seed edges:\n",
+    "edge_label_index = val_data.edge_label_index\n",
+    "edge_label = val_data.edge_label\n",
+    "val_loader = LinkNeighborLoader(\n",
+    "    data=val_data,\n",
+    "    num_neighbors=[20, 20],\n",
+    "    edge_label_index=edge_label_index,\n",
+    "    edge_label=edge_label,\n",
+    "    batch_size=128,\n",
+    "    shuffle=False,\n",
+    ")\n",
+    "sampled_data = next(iter(val_loader))\n",
+    "sampled_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8Oz9AzOrVs2i",
+    "outputId": "3559ce46-8876-4f38-da29-e883104390c7"
+   },
+   "outputs": [],
+   "source": [
+    "preds = []\n",
+    "ground_truths = []\n",
+    "\n",
+    "for batch in tqdm(val_loader):\n",
+    "    with torch.no_grad():\n",
+    "        batch.to(device)\n",
+    "\n",
+    "        # compute predictions\n",
+    "        node_embeddings = sage_model(batch.x, batch.edge_index)\n",
+    "        scores = predictor(node_embeddings, batch.edge_label_index)\n",
+    "\n",
+    "        preds.append(scores)\n",
+    "        ground_truths.append(batch.edge_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yaV6LzVpZXaW",
+    "outputId": "44b93179-7a0c-4ef1-f918-e2b6a93d8735"
+   },
+   "outputs": [],
+   "source": [
+    "def normalize(scores):\n",
+    "  return (scores - scores.min()) / (scores.max() - scores.min())\n",
+    "\n",
+    "pred = torch.cat(preds, dim=0).cpu().numpy()\n",
+    "ground_truth = torch.cat(ground_truths, dim=0).cpu().numpy()\n",
+    "\n",
+    "pred = normalize(pred) > 0.5\n",
+    "ground_truth = normalize(ground_truth) > 0.5\n",
+    "\n",
+    "f1, prec, rec = compute_scores(ground_truth, pred)\n",
+    "\n",
+    "print(f'F1 Score: {f1}')\n",
+    "print(f'Precision: {prec}')\n",
+    "print(f'Recall: {rec}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "k072d2xfahBN"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "chap7",
+   "language": "python",
+   "name": "chap7"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter07/03_supervised_classsification_graphSAGE-TFIDF.ipynb b/Chapter07/03_supervised_classsification_graphSAGE-TFIDF.ipynb
deleted file mode 100644
index 0058503..0000000
--- a/Chapter07/03_supervised_classsification_graphSAGE-TFIDF.ipynb
+++ /dev/null
@@ -1,1884 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Graph Neural Network Topic Classifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following we will focus on building a model for topic classification based on a Graph Neural Network approach.\n",
-    "\n",
-    "In particular in the following we will show you how to:\n",
-    "\n",
-    "* Create a TF-IDF representation of the corpus, that will be used as node features in the Graph Neural Network model \n",
-    "* Build, train a Graph Neural Network model and identify the best threshold for classifying documents \n",
-    "* Test the performance of the model in a out-of-sample tests, following a truly inductive approach \n",
-    "\n",
-    "**NOTE: This Notebook can only be run after the 01_nlp_graph_creation notebook, as some of the results computed in the first notebook will be here reused.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = pd.read_pickle(\"corpus.p\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>label</th>\n",
-       "      <th>clean_text</th>\n",
-       "      <th>parsed</th>\n",
-       "      <th>language</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>[trade]</td>\n",
-       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
-       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>[grain]</td>\n",
-       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
-       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>[crude, nat-gas]</td>\n",
-       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
-       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
-       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
-       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
-       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14833</th>\n",
-       "      <td>[palm-oil, veg-oil]</td>\n",
-       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
-       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
-       "      <td>en</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                     label  \\\n",
-       "id                                                           \n",
-       "test/14826                                         [trade]   \n",
-       "test/14828                                         [grain]   \n",
-       "test/14829                                [crude, nat-gas]   \n",
-       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]   \n",
-       "test/14833                             [palm-oil, veg-oil]   \n",
-       "\n",
-       "                                                   clean_text  \\\n",
-       "id                                                              \n",
-       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
-       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
-       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
-       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
-       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
-       "\n",
-       "                                                       parsed language  \n",
-       "id                                                                      \n",
-       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...       en  \n",
-       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...       en  \n",
-       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...       en  \n",
-       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...       en  \n",
-       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...       en  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corpus.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "topics = Counter([label for document_labels in corpus[\"label\"] for label in document_labels]).most_common(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('earn', 3964),\n",
-       " ('acq', 2369),\n",
-       " ('money-fx', 717),\n",
-       " ('grain', 582),\n",
-       " ('crude', 578),\n",
-       " ('trade', 485),\n",
-       " ('interest', 478),\n",
-       " ('ship', 286),\n",
-       " ('wheat', 283),\n",
-       " ('corn', 237)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topicsList = [topic[0] for topic in topics]\n",
-    "topicsSet = set(topicsList)\n",
-    "dataset = corpus[corpus[\"label\"].apply(lambda x: len(topicsSet.intersection(x))>0)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels(corpus, topicsList=topicsList):\n",
-    "    return corpus[\"label\"].apply(\n",
-    "        lambda labels: pd.Series({label: 1 for label in labels}).reindex(topicsList).fillna(0)\n",
-    "    )[topicsList]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = get_labels(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>earn</th>\n",
-       "      <th>acq</th>\n",
-       "      <th>money-fx</th>\n",
-       "      <th>grain</th>\n",
-       "      <th>crude</th>\n",
-       "      <th>trade</th>\n",
-       "      <th>interest</th>\n",
-       "      <th>ship</th>\n",
-       "      <th>wheat</th>\n",
-       "      <th>corn</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/14826</th>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14828</th>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>test/14829</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>test/14832</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/14839</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            earn  acq  money-fx  grain  crude  trade  interest  ship  wheat  \\\n",
-       "id                                                                            \n",
-       "test/14826   0.0  0.0       0.0    0.0    0.0    1.0       0.0   0.0    0.0   \n",
-       "test/14828   0.0  0.0       0.0    1.0    0.0    0.0       0.0   0.0    0.0   \n",
-       "test/14829   0.0  0.0       0.0    0.0    1.0    0.0       0.0   0.0    0.0   \n",
-       "test/14832   0.0  0.0       0.0    1.0    0.0    1.0       0.0   0.0    0.0   \n",
-       "test/14839   0.0  0.0       0.0    0.0    0.0    0.0       0.0   1.0    0.0   \n",
-       "\n",
-       "            corn  \n",
-       "id                \n",
-       "test/14826   0.0  \n",
-       "test/14828   0.0  \n",
-       "test/14829   0.0  \n",
-       "test/14832   1.0  \n",
-       "test/14839   0.0  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_features(corpus):\n",
-    "    return corpus[\"parsed\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_features_and_labels(corpus):\n",
-    "    return get_features(corpus), get_labels(corpus)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_test_split(corpus):\n",
-    "    train_idx = [idx for idx in corpus.index if \"training/\" in idx]\n",
-    "    test_idx = [idx for idx in corpus.index if \"test/\" in idx]\n",
-    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def my_spacy_tokenizer(pos_filter=[\"NOUN\", \"VERB\", \"PROPN\"]):\n",
-    "    def tokenizer(doc):\n",
-    "        return [token.lemma_ for token in doc if (pos_filter is None) or (token.pos_ in pos_filter)] \n",
-    "    return tokenizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.feature_extraction.text import TfidfVectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cntVectorizer = TfidfVectorizer(\n",
-    "    analyzer=my_spacy_tokenizer(),\n",
-    "    max_df = 0.25, min_df = 2, max_features = 10000\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainFeatures, _ = get_features_and_labels(train)\n",
-    "testFeatures, _ = get_features_and_labels(test)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainedTransformed = cntVectorizer.fit_transform(trainFeatures)\n",
-    "testTransformed = cntVectorizer.transform(testFeatures)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features = pd.concat([\n",
-    "    pd.DataFrame.sparse.from_spmatrix(trainedTransformed, index=trainFeatures.index), \n",
-    "    pd.DataFrame.sparse.from_spmatrix(testTransformed, index=testFeatures.index)\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(9034, 10000)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Creating the Graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import stellargraph as sg\n",
-    "from stellargraph import StellarGraph, IndexedArray\n",
-    "from stellargraph.mapper import GraphSAGENodeGenerator\n",
-    "from stellargraph.layer import GraphSAGE\n",
-    "\n",
-    "from tensorflow.keras import layers, optimizers, losses, metrics, Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edges = pd.read_pickle(\"bipartiteEdges.p\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entityTypes = {entity: ith for ith, entity in enumerate(edges[\"type\"].unique())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'keywords': 0, 'GPE': 1, 'ORG': 2, 'PERSON': 3}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "entityTypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documentFeatures = features.loc[set(corpus.index).intersection(features.index)] #.assign(document=1, entity=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>9990</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/5938</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/21465</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 10000 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               0     1     2     3     4     5     6     7     8     9     \\\n",
-       "id                                                                          \n",
-       "training/9238   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "test/15296      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "test/15287      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "training/5938   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "test/21465      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "\n",
-       "               ...  9990  9991  9992  9993  9994  9995  9996  9997  9998  9999  \n",
-       "id             ...                                                              \n",
-       "training/9238  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
-       "test/15296     ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
-       "test/15287     ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
-       "training/5938  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
-       "test/21465     ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
-       "\n",
-       "[5 rows x 10000 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documentFeatures.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entities = edges.groupby([\"target\", \"type\"])[\"source\"].count().groupby(level=0).apply(\n",
-    "    lambda s: s.droplevel(0).reindex(entityTypes.keys()).fillna(0)\n",
-    ").unstack(level=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "entityFeatures = (entities.T / entities.sum(axis=1)).T.assign(document=0, entity=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nodes = {\"entity\": entityFeatures, \n",
-    "         \"document\": documentFeatures}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stellarGraph = StellarGraph(nodes, \n",
-    "                            edges[edges[\"source\"].isin(documentFeatures.index)], \n",
-    "                            edge_type_column=\"type\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 23998, Edges: 86849\n",
-      "\n",
-      " Node types:\n",
-      "  entity: [14964]\n",
-      "    Features: float32 vector, length 6\n",
-      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
-      "  document: [9034]\n",
-      "    Features: float32 vector, length 10000\n",
-      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
-      "\n",
-      " Edge types:\n",
-      "    document-keywords->entity: [78838]\n",
-      "        Weights: range=[0.0827011, 1], mean=0.258464, std=0.0898612\n",
-      "        Features: none\n",
-      "    document-ORG->entity: [4129]\n",
-      "        Weights: range=[2, 22], mean=3.24122, std=2.30508\n",
-      "        Features: none\n",
-      "    document-GPE->entity: [2943]\n",
-      "        Weights: range=[2, 25], mean=3.25926, std=2.07008\n",
-      "        Features: none\n",
-      "    document-PERSON->entity: [939]\n",
-      "        Weights: range=[2, 14], mean=2.97444, std=1.65956\n",
-      "        Features: none\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(stellarGraph.info())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from stellargraph.data import EdgeSplitter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splitter = EdgeSplitter(stellarGraph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "** Sampled 17369 positive and 17369 negative edges. **\n"
-     ]
-    }
-   ],
-   "source": [
-    "graphTest, samplesTest, labelsTest = splitter.train_test_split(p=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 23998, Edges: 86849\n",
-      "\n",
-      " Node types:\n",
-      "  entity: [14964]\n",
-      "    Features: float32 vector, length 6\n",
-      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
-      "  document: [9034]\n",
-      "    Features: float32 vector, length 10000\n",
-      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
-      "\n",
-      " Edge types:\n",
-      "    document-keywords->entity: [78838]\n",
-      "        Weights: range=[0.0827011, 1], mean=0.258464, std=0.0898612\n",
-      "        Features: none\n",
-      "    document-ORG->entity: [4129]\n",
-      "        Weights: range=[2, 22], mean=3.24122, std=2.30508\n",
-      "        Features: none\n",
-      "    document-GPE->entity: [2943]\n",
-      "        Weights: range=[2, 25], mean=3.25926, std=2.07008\n",
-      "        Features: none\n",
-      "    document-PERSON->entity: [939]\n",
-      "        Weights: range=[2, 14], mean=2.97444, std=1.65956\n",
-      "        Features: none\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(stellarGraph.info())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 23998, Edges: 69480\n",
-      "\n",
-      " Node types:\n",
-      "  entity: [14964]\n",
-      "    Features: float32 vector, length 6\n",
-      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
-      "  document: [9034]\n",
-      "    Features: float32 vector, length 10000\n",
-      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
-      "\n",
-      " Edge types:\n",
-      "    document-keywords->entity: [63057]\n",
-      "        Weights: range=[0.0827011, 1], mean=0.258427, std=0.0899773\n",
-      "        Features: none\n",
-      "    document-ORG->entity: [3296]\n",
-      "        Weights: range=[2, 22], mean=3.21572, std=2.2592\n",
-      "        Features: none\n",
-      "    document-GPE->entity: [2360]\n",
-      "        Weights: range=[2, 19], mean=3.24237, std=2.01535\n",
-      "        Features: none\n",
-      "    document-PERSON->entity: [767]\n",
-      "        Weights: range=[2, 14], mean=3, std=1.69163\n",
-      "        Features: none\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(graphTest.info())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a Topic Classification Model "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start by splitting the data into train, validation and test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "targets = labels.reindex(documentFeatures.index).fillna(0)\n",
-    "#documentFeatures.drop([\"entity\", \"document\"], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>earn</th>\n",
-       "      <th>acq</th>\n",
-       "      <th>money-fx</th>\n",
-       "      <th>grain</th>\n",
-       "      <th>crude</th>\n",
-       "      <th>trade</th>\n",
-       "      <th>interest</th>\n",
-       "      <th>ship</th>\n",
-       "      <th>wheat</th>\n",
-       "      <th>corn</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>test/16678</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test/15913</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/12032</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/8366</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>training/10454</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                earn  acq  money-fx  grain  crude  trade  interest  ship  \\\n",
-       "id                                                                         \n",
-       "test/16678       1.0  0.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
-       "test/15913       1.0  0.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
-       "training/12032   0.0  1.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
-       "training/8366    1.0  0.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
-       "training/10454   0.0  1.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
-       "\n",
-       "                wheat  corn  \n",
-       "id                           \n",
-       "test/16678        0.0   0.0  \n",
-       "test/15913        0.0   0.0  \n",
-       "training/12032    0.0   0.0  \n",
-       "training/8366     0.0   0.0  \n",
-       "training/10454    0.0   0.0  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "targets.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_test_split(corpus):\n",
-    "    graphIndex = [index for index in corpus.index]\n",
-    "    \n",
-    "    train_idx = [idx for idx in graphIndex if \"training/\" in idx]\n",
-    "    test_idx = [idx for idx in graphIndex if \"test/\" in idx]\n",
-    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sampled, hold_out = train_test_split(targets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allNeighbors = np.unique([n for node in sampled.index for n in stellarGraph.neighbors(node)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subgraph = stellarGraph.subgraph(set(sampled.index).union(allNeighbors))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "StellarGraph: Undirected multigraph\n",
-      " Nodes: 16927, Edges: 62454\n",
-      "\n",
-      " Node types:\n",
-      "  entity: [10438]\n",
-      "    Features: float32 vector, length 6\n",
-      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
-      "  document: [6489]\n",
-      "    Features: float32 vector, length 10000\n",
-      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
-      "\n",
-      " Edge types:\n",
-      "    document-keywords->entity: [56647]\n",
-      "        Weights: range=[0.0918226, 1], mean=0.25739, std=0.0888008\n",
-      "        Features: none\n",
-      "    document-ORG->entity: [3032]\n",
-      "        Weights: range=[2, 22], mean=3.20877, std=2.21143\n",
-      "        Features: none\n",
-      "    document-GPE->entity: [2104]\n",
-      "        Weights: range=[2, 25], mean=3.25808, std=2.08119\n",
-      "        Features: none\n",
-      "    document-PERSON->entity: [671]\n",
-      "        Weights: range=[2, 14], mean=2.97615, std=1.66958\n",
-      "        Features: none\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(subgraph.info())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "train, leftOut = train_test_split(\n",
-    "    sampled,\n",
-    "    train_size=0.1,\n",
-    "    test_size=None,\n",
-    "    random_state=42,\n",
-    ")\n",
-    "\n",
-    "validation, test = train_test_split(\n",
-    "    leftOut, train_size=0.2, test_size=None, random_state=100,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "validation = validation[validation.sum(axis=1) > 0]\n",
-    "test = test[test.sum(axis=1) > 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation: (1168, 10)\n",
-      "Test: (4673, 10)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Validation: {validation.shape}\")\n",
-    "print(f\"Test: {test.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start  by creating the model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 50\n",
-    "num_samples = [10, 5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from stellargraph.mapper import HinSAGENodeGenerator\n",
-    "\n",
-    "generator = HinSAGENodeGenerator(subgraph, batch_size, num_samples, head_node_type=\"document\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from stellargraph.layer import HinSAGE\n",
-    "\n",
-    "graphsage_model = HinSAGE(\n",
-    "    layer_sizes=[32, 32], generator=generator, bias=True, dropout=0.5,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_inp, x_out = graphsage_model.in_out_tensors()\n",
-    "prediction = layers.Dense(units=train.shape[1], activation=\"sigmoid\")(x_out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([None, 10])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prediction.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = Model(inputs=x_inp, outputs=prediction)\n",
-    "model.compile(\n",
-    "    optimizer=optimizers.Adam(lr=0.005),\n",
-    "    loss=losses.binary_crossentropy,\n",
-    "    metrics=[\"acc\"],\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now train the model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_gen = generator.flow(train.index, train, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_gen = generator.flow(validation.index, validation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "13/13 [==============================] - 215s 17s/step - loss: 0.6139 - acc: 0.1365 - val_loss: 0.4780 - val_acc: 0.4401\n",
-      "Epoch 2/50\n",
-      "13/13 [==============================] - 169s 13s/step - loss: 0.4675 - acc: 0.4323 - val_loss: 0.4001 - val_acc: 0.4401\n",
-      "Epoch 3/50\n",
-      "13/13 [==============================] - 162s 13s/step - loss: 0.3973 - acc: 0.4319 - val_loss: 0.3486 - val_acc: 0.4401\n",
-      "Epoch 4/50\n",
-      "13/13 [==============================] - 153s 12s/step - loss: 0.3447 - acc: 0.4604 - val_loss: 0.3124 - val_acc: 0.4401\n",
-      "Epoch 5/50\n",
-      "13/13 [==============================] - 144s 11s/step - loss: 0.3090 - acc: 0.4997 - val_loss: 0.2853 - val_acc: 0.4932\n",
-      "Epoch 6/50\n",
-      "13/13 [==============================] - 159s 13s/step - loss: 0.2886 - acc: 0.5484 - val_loss: 0.2639 - val_acc: 0.6045\n",
-      "Epoch 7/50\n",
-      "13/13 [==============================] - 187s 15s/step - loss: 0.2612 - acc: 0.6354 - val_loss: 0.2453 - val_acc: 0.6387\n",
-      "Epoch 8/50\n",
-      "13/13 [==============================] - 203s 16s/step - loss: 0.2509 - acc: 0.6294 - val_loss: 0.2307 - val_acc: 0.6404\n",
-      "Epoch 9/50\n",
-      "13/13 [==============================] - 178s 14s/step - loss: 0.2370 - acc: 0.6489 - val_loss: 0.2160 - val_acc: 0.6789\n",
-      "Epoch 10/50\n",
-      "13/13 [==============================] - 190s 15s/step - loss: 0.2155 - acc: 0.6836 - val_loss: 0.2046 - val_acc: 0.7029\n",
-      "Epoch 11/50\n",
-      "13/13 [==============================] - 172s 14s/step - loss: 0.2047 - acc: 0.7310 - val_loss: 0.1938 - val_acc: 0.7260\n",
-      "Epoch 12/50\n",
-      "13/13 [==============================] - 145s 12s/step - loss: 0.2009 - acc: 0.7208 - val_loss: 0.1846 - val_acc: 0.7509\n",
-      "Epoch 13/50\n",
-      "13/13 [==============================] - 167s 13s/step - loss: 0.1834 - acc: 0.7843 - val_loss: 0.1755 - val_acc: 0.7860\n",
-      "Epoch 14/50\n",
-      "13/13 [==============================] - 208s 17s/step - loss: 0.1787 - acc: 0.7943 - val_loss: 0.1679 - val_acc: 0.8005\n",
-      "Epoch 15/50\n",
-      "13/13 [==============================] - 216s 17s/step - loss: 0.1718 - acc: 0.8123 - val_loss: 0.1598 - val_acc: 0.8365\n",
-      "Epoch 16/50\n",
-      "13/13 [==============================] - 201s 16s/step - loss: 0.1619 - acc: 0.8612 - val_loss: 0.1531 - val_acc: 0.8416\n",
-      "Epoch 17/50\n",
-      "13/13 [==============================] - 173s 14s/step - loss: 0.1609 - acc: 0.8378 - val_loss: 0.1470 - val_acc: 0.8502\n",
-      "Epoch 18/50\n",
-      "13/13 [==============================] - 157s 12s/step - loss: 0.1496 - acc: 0.8471 - val_loss: 0.1412 - val_acc: 0.8690\n",
-      "Epoch 19/50\n",
-      "13/13 [==============================] - 155s 12s/step - loss: 0.1471 - acc: 0.8600 - val_loss: 0.1379 - val_acc: 0.8604\n",
-      "Epoch 20/50\n",
-      "13/13 [==============================] - 154s 12s/step - loss: 0.1366 - acc: 0.8801 - val_loss: 0.1318 - val_acc: 0.8767\n",
-      "Epoch 21/50\n",
-      "13/13 [==============================] - 155s 12s/step - loss: 0.1362 - acc: 0.8708 - val_loss: 0.1285 - val_acc: 0.8664\n",
-      "Epoch 22/50\n",
-      "13/13 [==============================] - 156s 12s/step - loss: 0.1361 - acc: 0.8546 - val_loss: 0.1259 - val_acc: 0.8682\n",
-      "Epoch 23/50\n",
-      "13/13 [==============================] - 154s 12s/step - loss: 0.1197 - acc: 0.9104 - val_loss: 0.1231 - val_acc: 0.8733\n",
-      "Epoch 24/50\n",
-      "13/13 [==============================] - 146s 11s/step - loss: 0.1240 - acc: 0.8834 - val_loss: 0.1175 - val_acc: 0.8844\n",
-      "Epoch 25/50\n",
-      "13/13 [==============================] - 131s 10s/step - loss: 0.1145 - acc: 0.9165 - val_loss: 0.1165 - val_acc: 0.8853\n",
-      "Epoch 26/50\n",
-      "13/13 [==============================] - 131s 10s/step - loss: 0.1216 - acc: 0.8844 - val_loss: 0.1155 - val_acc: 0.8784\n",
-      "Epoch 27/50\n",
-      "13/13 [==============================] - 132s 11s/step - loss: 0.1084 - acc: 0.9093 - val_loss: 0.1111 - val_acc: 0.8878\n",
-      "Epoch 28/50\n",
-      "13/13 [==============================] - 127s 10s/step - loss: 0.1039 - acc: 0.9156 - val_loss: 0.1095 - val_acc: 0.8853\n",
-      "Epoch 29/50\n",
-      "13/13 [==============================] - 128s 10s/step - loss: 0.1066 - acc: 0.9175 - val_loss: 0.1095 - val_acc: 0.8818\n",
-      "Epoch 30/50\n",
-      "13/13 [==============================] - 194s 16s/step - loss: 0.0987 - acc: 0.9199 - val_loss: 0.1089 - val_acc: 0.8784\n",
-      "Epoch 31/50\n",
-      "13/13 [==============================] - 194s 16s/step - loss: 0.0995 - acc: 0.9164 - val_loss: 0.1047 - val_acc: 0.8827\n",
-      "Epoch 32/50\n",
-      "13/13 [==============================] - 206s 16s/step - loss: 0.0938 - acc: 0.9322 - val_loss: 0.1030 - val_acc: 0.8818\n",
-      "Epoch 33/50\n",
-      "13/13 [==============================] - 199s 16s/step - loss: 0.0907 - acc: 0.9205 - val_loss: 0.1014 - val_acc: 0.8853\n",
-      "Epoch 34/50\n",
-      "13/13 [==============================] - 213s 17s/step - loss: 0.0918 - acc: 0.9208 - val_loss: 0.0990 - val_acc: 0.8887\n",
-      "Epoch 35/50\n",
-      "13/13 [==============================] - 264s 21s/step - loss: 0.0887 - acc: 0.9342 - val_loss: 0.0978 - val_acc: 0.8878\n",
-      "Epoch 36/50\n",
-      "13/13 [==============================] - 378s 30s/step - loss: 0.0875 - acc: 0.9170 - val_loss: 0.0956 - val_acc: 0.8955\n",
-      "Epoch 37/50\n",
-      "13/13 [==============================] - 247s 19s/step - loss: 0.0856 - acc: 0.9363 - val_loss: 0.0969 - val_acc: 0.8896\n",
-      "Epoch 38/50\n",
-      "13/13 [==============================] - 224s 17s/step - loss: 0.0777 - acc: 0.9312 - val_loss: 0.0938 - val_acc: 0.8921\n",
-      "Epoch 39/50\n",
-      "13/13 [==============================] - 201s 16s/step - loss: 0.0837 - acc: 0.9205 - val_loss: 0.0930 - val_acc: 0.8938\n",
-      "Epoch 40/50\n",
-      "13/13 [==============================] - 201s 16s/step - loss: 0.0844 - acc: 0.9180 - val_loss: 0.0917 - val_acc: 0.8938\n",
-      "Epoch 41/50\n",
-      "13/13 [==============================] - 197s 16s/step - loss: 0.0731 - acc: 0.9353 - val_loss: 0.0917 - val_acc: 0.8938\n",
-      "Epoch 42/50\n",
-      "13/13 [==============================] - 210s 17s/step - loss: 0.0732 - acc: 0.9220 - val_loss: 0.0908 - val_acc: 0.8861\n",
-      "Epoch 43/50\n",
-      "13/13 [==============================] - 236s 19s/step - loss: 0.0718 - acc: 0.9440 - val_loss: 0.0923 - val_acc: 0.8896\n",
-      "Epoch 44/50\n",
-      "13/13 [==============================] - 186s 15s/step - loss: 0.0711 - acc: 0.9581 - val_loss: 0.0912 - val_acc: 0.8861\n",
-      "Epoch 45/50\n",
-      "13/13 [==============================] - 169s 13s/step - loss: 0.0704 - acc: 0.9449 - val_loss: 0.0893 - val_acc: 0.8887\n",
-      "Epoch 46/50\n",
-      "13/13 [==============================] - 183s 15s/step - loss: 0.0768 - acc: 0.9366 - val_loss: 0.0897 - val_acc: 0.8887\n",
-      "Epoch 47/50\n",
-      "13/13 [==============================] - 196s 16s/step - loss: 0.0723 - acc: 0.9305 - val_loss: 0.0861 - val_acc: 0.8990\n",
-      "Epoch 48/50\n",
-      "13/13 [==============================] - 154s 12s/step - loss: 0.0733 - acc: 0.9289 - val_loss: 0.0873 - val_acc: 0.8964\n",
-      "Epoch 49/50\n",
-      "13/13 [==============================] - 228s 18s/step - loss: 0.0691 - acc: 0.9568 - val_loss: 0.0878 - val_acc: 0.8998\n",
-      "Epoch 50/50\n",
-      "13/13 [==============================] - 211s 17s/step - loss: 0.0625 - acc: 0.9409 - val_loss: 0.0864 - val_acc: 0.8896\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(\n",
-    "    train_gen, epochs=50, validation_data=val_gen, verbose=1, shuffle=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sg.utils.plot_history(history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history = model.fit(\n",
-    "    train_gen, epochs=50, validation_data=val_gen, verbose=1, shuffle=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sg.utils.plot_history(history)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Threshold identification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_gen = generator.flow(test.index, test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "94/94 [==============================] - 391s 4s/step - loss: 0.0933 - acc: 0.8795\n",
-      "\n",
-      "Test Set Metrics:\n",
-      "\tloss: 0.0933\n",
-      "\tacc: 0.8795\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_metrics = model.evaluate(test_gen)\n",
-    "print(\"\\nTest Set Metrics:\")\n",
-    "for name, val in zip(model.metrics_names, test_metrics):\n",
-    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_predictions = pd.DataFrame(model.predict(test_gen), index=test.index, columns=test.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_results = pd.concat({\n",
-    "    \"target\": test, \n",
-    "    \"preds\": test_predictions\n",
-    "}, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import f1_score, classification_report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f1s = {}\n",
-    "\n",
-    "for th in [0.01,0.05,0.1,0.2,0.3,0.4,0.5]:\n",
-    "    f1s[th] = f1_score(test_results[\"target\"], 1.0*(test_results[\"preds\"]>th), average=\"macro\")\n",
-    "    \n",
-    "pd.Series(f1s).plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As it can be seen, with a threshold of about 0.2 we obtain the best performances. We thus use this value for producing the classification report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.92      0.97      0.94      2075\n",
-      "           1       0.85      0.96      0.90      1200\n",
-      "           2       0.65      0.90      0.75       364\n",
-      "           3       0.83      0.95      0.89       305\n",
-      "           4       0.86      0.68      0.76       296\n",
-      "           5       0.74      0.56      0.63       269\n",
-      "           6       0.60      0.80      0.69       245\n",
-      "           7       0.62      0.10      0.17       150\n",
-      "           8       0.49      0.95      0.65       149\n",
-      "           9       0.44      0.88      0.58       129\n",
-      "\n",
-      "   micro avg       0.80      0.89      0.84      5182\n",
-      "   macro avg       0.70      0.78      0.70      5182\n",
-      "weighted avg       0.82      0.89      0.84      5182\n",
-      " samples avg       0.83      0.90      0.85      5182\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(classification_report(test_results[\"target\"], 1.0*(test_results[\"preds\"]>0.2)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Inductive Prediction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now provide a prediction truly inductive, thus we will be using the full graph and we will also use the threshold of 0.2 we have identified above as the one providing the top f1-score.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generator = HinSAGENodeGenerator(stellarGraph, batch_size, num_samples, head_node_type=\"document\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hold_out = hold_out[hold_out.sum(axis=1) > 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hold_out_gen = generator.flow(hold_out.index, hold_out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hold_out_predictions = model.predict(hold_out_gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = pd.DataFrame(1.0*(hold_out_predictions > 0.2), index=hold_out.index, columns=hold_out.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = pd.concat({\n",
-    "    \"target\": hold_out, \n",
-    "    \"preds\": preds\n",
-    "}, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.93      0.99      0.96      1087\n",
-      "           1       0.90      0.97      0.93       719\n",
-      "           2       0.64      0.92      0.76       179\n",
-      "           3       0.82      0.95      0.88       149\n",
-      "           4       0.85      0.62      0.72       189\n",
-      "           5       0.74      0.50      0.59       117\n",
-      "           6       0.60      0.79      0.68       131\n",
-      "           7       0.43      0.03      0.06        89\n",
-      "           8       0.50      0.96      0.66        71\n",
-      "           9       0.39      0.86      0.54        56\n",
-      "\n",
-      "   micro avg       0.82      0.89      0.85      2787\n",
-      "   macro avg       0.68      0.76      0.68      2787\n",
-      "weighted avg       0.83      0.89      0.84      2787\n",
-      " samples avg       0.84      0.90      0.86      2787\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(classification_report(results[\"target\"], results[\"preds\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ml-book-7",
-   "language": "python",
-   "name": "ml-book-7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
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new file mode 100644
index 0000000..26c785c
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
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+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "5447090a9e0c7bb4bf1e43596846c94e85705571a6c554f14de5f16f63528eb0"
diff --git a/Chapter07/pyproject.toml b/Chapter07/pyproject.toml
new file mode 100644
index 0000000..e3cac0e
--- /dev/null
+++ b/Chapter07/pyproject.toml
@@ -0,0 +1,44 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 7"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+# [[tool.poetry.source]]
+# name = "torch-wheels"
+# url = "https://data.pyg.org/whl/torch-2.1.0+cpu.html"
+# priority = "supplemental"
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+networkx = "==2.5" 
+scikit-learn = "==0.24.0" 
+gensim = "==3.8.3"
+node2vec = "==0.3.3"
+chardet = "==5.2.0"
+tensorflow = "^2.6.0" 
+tensorflow-io-gcs-filesystem = "==0.21.0" # This needs pinning - see https://stackoverflow.com/a/76477590 
+protobuf= "^3.20"
+torch = "^2.1.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+python-louvain = "==0.16"
+nxt-gem = { git="https://github.com/palash1992/GEM.git", branch="master" }
+# This is what is holding us back to python 3.8
+stellargraph = "^1.2.1"
+# Since 2024.06.27, DGL have stopped providing packages for Windows and MacOS. The latest version of available package is 2.2.1.
+dgl = {url = "https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl"}
+torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+pyg-lib = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/pyg_lib-0.4.0%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Chapter07/requirements.txt b/Chapter07/requirements.txt
index 14814ef..8eb8b77 100644
--- a/Chapter07/requirements.txt
+++ b/Chapter07/requirements.txt
@@ -1,18 +1,138 @@
-networkx==2.4  
-scikit-learn==0.24.0 
-stellargraph==1.2.1 
-spacy==3.0.3 
-pandas==1.1.3 
-numpy==1.19.2 
-node2vec==0.3.3 
-Keras==2.0.2 
-tensorflow==2.4.1 
-communities==2.2.0 
-gensim==3.8.3 
-matplotlib==3.3.4 
-nltk==3.5 
-langdetect==1.0.9
-fasttext==0.9.2
-python-louvain==0.15
-click==7.1.2
-smart-open==3.0.0 
+absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+aiohappyeyeballs==2.4.3 ; python_version >= "3.8" and python_version < "3.9"
+aiohttp==3.10.10 ; python_version >= "3.8" and python_version < "3.9"
+aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+annotated-types==0.7.0 ; python_version >= "3.8" and python_version < "3.9"
+appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9"
+async-timeout==4.0.3 ; python_version >= "3.8" and python_version < "3.9"
+attrs==24.2.0 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+cachetools==5.5.0 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+chardet==5.2.0 ; python_version >= "3.8" and python_version < "3.9"
+charset-normalizer==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+cython==0.29.14 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.7 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+dgl @ https://data.dgl.ai/wheels/torch-2.1/dgl-2.4.0-cp38-cp38-manylinux1_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+filelock==3.16.1 ; python_version >= "3.8" and python_version < "3.9"
+flatbuffers==2.0.7 ; python_version >= "3.8" and python_version < "3.9"
+frozenlist==1.4.1 ; python_version >= "3.8" and python_version < "3.9"
+fsspec==2024.9.0 ; python_version >= "3.8" and python_version < "3.9"
+gast==0.4.0 ; python_version >= "3.8" and python_version < "3.9"
+gensim==3.8.3 ; python_version >= "3.8" and python_version < "3.9"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.9"
+google-auth==2.35.0 ; python_version >= "3.8" and python_version < "3.9"
+google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+grpcio==1.66.2 ; python_version >= "3.8" and python_version < "3.9"
+h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.10 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.1 ; python_version >= "3.8" and python_version < "3.9"
+jinja2==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9"
+keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9"
+keras==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9"
+libclang==18.1.1 ; python_version >= "3.8" and python_version < "3.9"
+lightning-utilities==0.11.7 ; python_version >= "3.8" and python_version < "3.9"
+markdown==3.7 ; python_version >= "3.8" and python_version < "3.9"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9"
+multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9"
+networkx==2.5 ; python_version >= "3.8" and python_version < "3.9"
+node2vec==0.3.3 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.24.4 ; python_version >= "3.8" and python_version < "3.9"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nxt-gem @ git+https://github.com/palash1992/GEM.git@ae8e92d34213f5785757b4a0943bd7d8d337adb3 ; python_version >= "3.8" and python_version < "3.9"
+oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+packaging==24.1 ; python_version >= "3.8" and python_version < "3.9"
+pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9"
+propcache==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9"
+psutil==6.0.0 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pydantic-core==2.23.4 ; python_version >= "3.8" and python_version < "3.9"
+pydantic==2.9.2 ; python_version >= "3.8" and python_version < "3.9"
+pyg-lib @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/pyg_lib-0.4.0%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9"
+python-louvain==0.16 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==308 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyyaml==6.0.2 ; python_version >= "3.8" and python_version < "3.9"
+pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9"
+requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9"
+rsa==4.9 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==0.24.0 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+setuptools==75.1.0 ; python_version >= "3.8" and python_version < "3.9"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==7.0.5 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9"
+sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9"
+termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+theano==1.0.5 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9"
+torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9"
+torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9"
+torchmetrics==1.4.3 ; python_version >= "3.8" and python_version < "3.9"
+torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.66.5 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9"
+tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9"
+werkzeug==3.0.4 ; python_version >= "3.8" and python_version < "3.9"
+wheel==0.44.0 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+yarl==1.15.2 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter08/01_Credit_card_edges_classification.ipynb b/Chapter08/01_Credit_card_edges_classification.ipynb
deleted file mode 100644
index d71f496..0000000
--- a/Chapter08/01_Credit_card_edges_classification.ipynb
+++ /dev/null
@@ -1,563 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Z12nIL0GmtKF"
-   },
-   "source": [
-    "# Machine learning for Credit Card Transactions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Plnw0bRc_Mks"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import math\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "default_edge_color = 'gray'\n",
-    "default_node_color = '#407cc9'\n",
-    "enhanced_node_color = '#f5b042'\n",
-    "enhanced_edge_color = '#cc2f04'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Vye1SbKAnI_A"
-   },
-   "source": [
-    "## Load Dataset and build graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "df = pd.read_csv(\"/Users/claudiostamile/Downloads/fraudTrain.csv\")\n",
-    "df = df[df[\"is_fraud\"]==0].sample(frac=0.20, random_state=42).append(df[df[\"is_fraud\"] == 1])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"is_fraud\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def build_graph_bipartite(df_input, graph_type=nx.Graph()):\n",
-    "    df = df_input.copy()\n",
-    "    mapping = {x:node_id for node_id,x in enumerate(set(df[\"cc_num\"].values.tolist() + df[\"merchant\"].values.tolist()))}\n",
-    "    df[\"from\"] = df[\"cc_num\"].apply(lambda x: mapping[x])\n",
-    "    df[\"to\"] = df[\"merchant\"].apply(lambda x: mapping[x])\n",
-    "    df = df[['from', 'to', \"amt\", \"is_fraud\"]].groupby(['from', 'to']).agg({\"is_fraud\": \"sum\", \"amt\": \"sum\"}).reset_index()\n",
-    "    df[\"is_fraud\"] = df[\"is_fraud\"].apply(lambda x: 1 if x>0 else 0)\n",
-    "    \n",
-    "    G = nx.from_edgelist(df[[\"from\", \"to\"]].values, create_using=graph_type)\n",
-    "    \n",
-    "    nx.set_node_attributes(G,{x:1 for x in df[\"from\"].unique()}, \"bipartite\")\n",
-    "    nx.set_node_attributes(G,{x:2 for x in df[\"to\"].unique()}, \"bipartite\")\n",
-    "    \n",
-    "    nx.set_edge_attributes(G, \n",
-    "                       {(int(x[\"from\"]), int(x[\"to\"])):x[\"is_fraud\"] for idx, x in df[[\"from\",\"to\",\"is_fraud\"]].iterrows()}, \n",
-    "                       \"label\")\n",
-    "\n",
-    "    nx.set_edge_attributes(G, \n",
-    "                       {(int(x[\"from\"]), int(x[\"to\"])):x[\"amt\"] for idx, x in df[[\"from\",\"to\",\"amt\"]].iterrows()}, \n",
-    "                       \"weight\")\n",
-    "    return G"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def build_graph_tripartite(df_input, graph_type=nx.Graph()):\n",
-    "    df = df_input.copy()\n",
-    "    mapping = {x:node_id for node_id,x in enumerate(set(df.index.values.tolist() + \n",
-    "                                                        df[\"cc_num\"].values.tolist() + \n",
-    "                                                        df[\"merchant\"].values.tolist()))}\n",
-    "    df[\"in_node\"] = df[\"cc_num\"].apply(lambda x: mapping[x])\n",
-    "    df[\"out_node\"] = df[\"merchant\"].apply(lambda x: mapping[x])\n",
-    "\n",
-    "    G = nx.from_edgelist([(x[\"in_node\"], mapping[idx]) for idx, x in df.iterrows()] +\n",
-    "                         [(x[\"out_node\"], mapping[idx]) for idx, x in df.iterrows()], \n",
-    "                         create_using=graph_type)\n",
-    "\n",
-    "    nx.set_node_attributes(G,{x[\"in_node\"]:1 for idx,x in df.iterrows()}, \"bipartite\")\n",
-    "    nx.set_node_attributes(G,{x[\"out_node\"]:2 for idx,x in df.iterrows()}, \"bipartite\")\n",
-    "    nx.set_node_attributes(G,{mapping[idx]:3 for idx, x in df.iterrows()}, \"bipartite\")\n",
-    "\n",
-    "    nx.set_edge_attributes(G,{(x[\"in_node\"], mapping[idx]):x[\"is_fraud\"] for idx, x in df.iterrows()}, \"label\")\n",
-    "    nx.set_edge_attributes(G,{(x[\"out_node\"], mapping[idx]):x[\"is_fraud\"] for idx, x in df.iterrows()}, \"label\")\n",
-    "\n",
-    "    nx.set_edge_attributes(G,{(x[\"in_node\"], mapping[idx]):x[\"amt\"] for idx, x in df.iterrows()}, \"weight\")\n",
-    "    nx.set_edge_attributes(G,{(x[\"out_node\"], mapping[idx]):x[\"amt\"] for idx, x in df.iterrows()}, \"weight\")\n",
-    "    return G"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G = build_graph_bipartite(df, nx.Graph())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from networkx.algorithms import bipartite\n",
-    "bipartite.is_bipartite(G)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "top = nx.bipartite.sets(G)[0]\n",
-    "pos = nx.bipartite_layout(G, top)\n",
-    "nx.draw(G, pos=pos, with_labels=False, node_color=default_node_color, edge_color=default_edge_color)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.axis(\"off\")\n",
-    "plt.figure(figsize=(10,10))\n",
-    "\n",
-    "nx.draw_networkx(G, pos=spring_pos, node_color=default_node_color, \n",
-    "                 edges_color=default_edge_color, with_labels=False, node_size=15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2z2PCthzneat"
-   },
-   "source": [
-    "## Network Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(nx.info(G))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "degrees = pd.Series({k: v for k, v in nx.degree(G)})\n",
-    "degrees.plot.hist()\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in G.edges(data=True)})\n",
-    "np.quantile(allEdgesWeights.values,[0.10,0.50,0.70,0.9,1.0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quant_dist = np.quantile(allEdgesWeights.values,[0.10,0.50,0.70,0.9])\n",
-    "quant_dist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "allEdgesWeightsFiltered = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in G.edges(data=True) \n",
-    "                                     if d[2][\"weight\"] < quant_dist[-1]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "allEdgesWeightsFiltered.plot.hist(bins=40)\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,10))\n",
-    "bC = nx.betweenness_centrality(G)\n",
-    "bc_distr = pd.Series(bC)\n",
-    "bc_distr.plot.hist()\n",
-    "plt.yscale(\"log\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "gPMC9VDyuF5F",
-    "outputId": "871111c8-12b4-4820-8675-f74fccdd39e6",
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "np.mean(list(bC.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "94viGU4vserg",
-    "outputId": "ea65df57-df57-4e51-f396-9c808f274766"
-   },
-   "outputs": [],
-   "source": [
-    "# degree centrality\n",
-    "plt.figure(figsize=(10,10))\n",
-    "deg_C = nx.degree_centrality(G)\n",
-    "degc_distr = pd.Series(deg_C)\n",
-    "degc_distr.plot.hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "vLp2CBJHtC1d",
-    "outputId": "c1ad4b5d-4d77-4ac1-84ce-6210fd7dc11f"
-   },
-   "outputs": [],
-   "source": [
-    "# closeness centrality\n",
-    "plt.figure(figsize=(10,10))\n",
-    "clos_C = nx.closeness_centrality(G)\n",
-    "closc_distr = pd.Series(clos_C)\n",
-    "closc_distr.plot.hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.mean(list(clos_C.values()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "MQOah_yDtbaW",
-    "outputId": "558fc1ea-f457-4386-b5ce-8dc61f8f0115"
-   },
-   "outputs": [],
-   "source": [
-    "# assortativity\n",
-    "nx.degree_pearson_correlation_coefficient(G)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "c8peWeN9nh1m"
-   },
-   "source": [
-    "### Community Detection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import community\n",
-    "\n",
-    "parts = community.best_partition(G, random_state=42, weight='weight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "communities = pd.Series(parts)\n",
-    "communities.value_counts().sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graphs = []\n",
-    "d = {}\n",
-    "for x in communities.unique():\n",
-    "    tmp = nx.subgraph(G, communities[communities==x].index)\n",
-    "    fraud_edges = sum(nx.get_edge_attributes(tmp, \"label\").values())\n",
-    "    ratio = 0 if fraud_edges == 0 else (fraud_edges/tmp.number_of_edges())*100\n",
-    "    d[x] = ratio\n",
-    "    graphs += [tmp]\n",
-    "\n",
-    "pd.Series(d).sort_values(ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gId = 10\n",
-    "plt.figure(figsize=(10,10))\n",
-    "spring_pos = nx.spring_layout(graphs[gId])\n",
-    "plt.axis(\"off\")\n",
-    "edge_colors = [\"r\" if x == 1 else \"g\" for x in nx.get_edge_attributes(graphs[gId], 'label').values()]\n",
-    "nx.draw_networkx(graphs[gId], pos=spring_pos, node_color=default_node_color, \n",
-    "                 edge_color=edge_colors, with_labels=False, node_size=15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Supervised Learning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.utils import resample\n",
-    "\n",
-    "df_majority = df[df.is_fraud==0]\n",
-    "df_minority = df[df.is_fraud==1]\n",
-    "\n",
-    "df_maj_dowsampled = resample(df_majority,\n",
-    "                             n_samples=len(df_minority),\n",
-    "                             random_state=42)\n",
-    "\n",
-    "df_downsampled = pd.concat([df_minority, df_maj_dowsampled])\n",
-    "\n",
-    "print(df_downsampled.is_fraud.value_counts())\n",
-    "G_down = build_graph_bipartite(df_downsampled)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "\n",
-    "train_edges, test_edges, train_labels, test_labels = train_test_split(list(range(len(G_down.edges))), \n",
-    "                                                                      list(nx.get_edge_attributes(G_down, \"label\").values()), \n",
-    "                                                                      test_size=0.20, \n",
-    "                                                                      random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "edgs = list(G_down.edges)\n",
-    "train_graph = G_down.edge_subgraph([edgs[x] for x in train_edges]).copy()\n",
-    "train_graph.add_nodes_from(list(set(G_down.nodes) - set(train_graph.nodes)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from node2vec import Node2Vec\n",
-    "from node2vec.edges import HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder\n",
-    "\n",
-    "node2vec_train = Node2Vec(train_graph, weight_key='weight')\n",
-    "model_train = node2vec_train.fit(window=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier \n",
-    "from sklearn import metrics \n",
-    "\n",
-    "classes = [HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder]\n",
-    "for cl in classes:\n",
-    "    embeddings_train = cl(keyed_vectors=model_train.wv) \n",
-    "\n",
-    "    train_embeddings = [embeddings_train[str(edgs[x][0]), str(edgs[x][1])] for x in train_edges]\n",
-    "    test_embeddings = [embeddings_train[str(edgs[x][0]), str(edgs[x][1])] for x in test_edges]\n",
-    "    \n",
-    "    rf = RandomForestClassifier(n_estimators=1000, random_state=42) \n",
-    "    rf.fit(train_embeddings, train_labels); \n",
-    "\n",
-    "    y_pred = rf.predict(test_embeddings)\n",
-    "    print(cl)\n",
-    "    print('Precision:', metrics.precision_score(test_labels, y_pred)) \n",
-    "    print('Recall:', metrics.recall_score(test_labels, y_pred)) \n",
-    "    print('F1-Score:', metrics.f1_score(test_labels, y_pred)) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "R4Vk5GnxcWF2"
-   },
-   "source": [
-    "## Unupervised Learning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nod2vec_unsup = Node2Vec(G_down, weight_key='weight')\n",
-    "unsup_vals = nod2vec_unsup.fit(window=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "classes = [HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder]\n",
-    "true_labels = [x for x in nx.get_edge_attributes(G_down, \"label\").values()]\n",
-    "\n",
-    "for cl in classes:\n",
-    "    embedding_edge = cl(keyed_vectors=unsup_vals.wv) \n",
-    "\n",
-    "    embedding = [embedding_edge[str(x[0]), str(x[1])] for x in G_down.edges()]\n",
-    "    kmeans = KMeans(2, random_state=42).fit(embedding)\n",
-    "    \n",
-    "    \n",
-    "    nmi = metrics.adjusted_mutual_info_score(true_labels, kmeans.labels_)\n",
-    "    ho = metrics.homogeneity_score(true_labels, kmeans.labels_)\n",
-    "    co = metrics.completeness_score(true_labels, kmeans.labels_)\n",
-    "    vmeasure = metrics.v_measure_score(true_labels, kmeans.labels_)\n",
-    "    \n",
-    "    print(cl)\n",
-    "    print('NMI:', nmi)\n",
-    "    print('Homogeneity:', ho)\n",
-    "    print('Completeness:', co)\n",
-    "    print('V-Measure:', vmeasure)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "socialNetwork.ipynb",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/Chapter08/01_nlp_graph_creation.ipynb b/Chapter08/01_nlp_graph_creation.ipynb
new file mode 100644
index 0000000..c0e3ffa
--- /dev/null
+++ b/Chapter08/01_nlp_graph_creation.ipynb
@@ -0,0 +1,4814 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text Analytics and Natural Language Processing using Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following we will focus on analyzing textual documents and leverage on graph analysis in order to identify insight and extract relevant information. \n",
+    "\n",
+    "In particular in the following we will show you how to:\n",
+    "\n",
+    "* Extract structured information from text by using NLP techniques and models\n",
+    "* Build different type of graphs starting from the information extracted in the previous point\n",
+    "* Analyze the graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nltk "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import pandas as pd\n",
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package reuters to /home/deusebio/nltk_data...\n",
+      "[nltk_data]   Package reuters is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.download('reuters')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import reuters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = pd.DataFrame([\n",
+    "    {\"id\": _id, \"clean_text\": reuters.raw(_id).replace(\"\\n\", \"\"), \"label\": reuters.categories(_id)}\n",
+    "    for _id in reuters.fileids()\n",
+    "]).set_index(\"id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SUBROTO SAYS INDONESIA SUPPORTS TIN PACT EXTENSION  Mines and Energy Minister Subroto  confirmed Indonesian support for an extension of the sixth  International Tin Agreement (ITA), but said a new pact was not  necessary.      Asked by Reuters to clarify his statement on Monday in  which he said the pact should be allowed to lapse, Subroto said  Indonesia was ready to back extension of the ITA.      \"We can support extension of the sixth agreement,\" he said.  \"But a seventh accord we believe to be unnecessary.\"      The sixth ITA will expire at the end of June unless a  two-thirds majority of members vote for an extension.  '"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.iloc[10][\"clean_text\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "len(Counter([label for document_labels in corpus[\"label\"] for label in document_labels]).most_common())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label  \n",
+       "id                                                          \n",
+       "test/14826                                         [trade]  \n",
+       "test/14828                                         [grain]  \n",
+       "test/14829                                [crude, nat-gas]  \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]  \n",
+       "test/14833                             [palm-oil, veg-oil]  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Language Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langdetect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def getLanguage(text: str):\n",
+    "    try:\n",
+    "        return langdetect.detect(text)\n",
+    "    except: \n",
+    "        return np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[\"language\"] = corpus[\"clean_text\"].apply(getLanguage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "language\n",
+       "en    9900\n",
+       "sv     429\n",
+       "de     373\n",
+       "sw      29\n",
+       "so      23\n",
+       "nl       9\n",
+       "pt       8\n",
+       "vi       5\n",
+       "et       4\n",
+       "sl       3\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus[\"language\"].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \n",
+       "id                                                                   \n",
+       "test/14826                                         [trade]       en  \n",
+       "test/14828                                         [grain]       en  \n",
+       "test/14829                                [crude, nat-gas]       en  \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en  \n",
+       "test/14833                             [palm-oil, veg-oil]       en  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using fasttext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "100  916k  100  916k    0     0  1364k      0 --:--:-- --:--:-- --:--:-- 1363k\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -w GET https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz > lid.176.ftz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import fasttext\n",
+    "\n",
+    "m = fasttext.load_model(\"lid.176.ftz\")\n",
+    "def getLanguage(text: str):\n",
+    "    return m.predict(text)[0][0].replace(\"__label__\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[\"language\"] = corpus[\"clean_text\"].apply(getLanguage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "language\n",
+       "en    10278\n",
+       "de       90\n",
+       "ja       73\n",
+       "it       67\n",
+       "sv       52\n",
+       "zh       48\n",
+       "es       31\n",
+       "fr       27\n",
+       "eu       20\n",
+       "eo       12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus[\"language\"].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "language\n",
+       "en    10278\n",
+       "de       90\n",
+       "ja       73\n",
+       "it       67\n",
+       "sv       52\n",
+       "zh       48\n",
+       "es       31\n",
+       "fr       27\n",
+       "eu       20\n",
+       "eo       12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus[\"language\"].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'USDA - U.S. 1986/87 ENDING CORN STOCKS  5,240 MLN BU, WHEAT 1,848 MLN, SOYBEANS 610 MLN  USDA - U.S. 1986/87 ENDING CORN STOCKS  5,240 MLN BU, WHEAT 1,848 MLN, SOYBEANS 610 MLN  '"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus[corpus[\"language\"]==\"ja\"].iloc[5][\"clean_text\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NLP Enrichment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to download the model from the Spacy library, please issue the following command in a shell \n",
+    "\n",
+    "\n",
+    "<pre><code>\n",
+    "python -m spacy download en_core_web_md\n",
+    "</code></pre>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nlp = spacy.load('en_core_web_md')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[\"parsed\"] = corpus[\"clean_text\"].apply(nlp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Thailand's trade deficit widened to 4.5  billion baht in the first quarter of 1987 from 2.1 billion a  year ago, the Business Economics Department said.      It said Janunary/March imports rose to 65.1 billion baht  from 58.7 billion. Thailand's improved business climate this  year resulted in a 27 pct increase in imports of raw materials  and semi-finished products.      The country's oil import bill, however, fell 23 pct in the  first quarter due to lower oil prices.      The department said first quarter exports expanded to 60.6  billion baht from 56.6 billion.      Export growth was smaller than expected due to lower  earnings from many key commodities including rice whose  earnings declined 18 pct, maize 66 pct, sugar 45 pct, tin 26  pct and canned pineapples seven pct.      Products registering high export growth were jewellery up  64 pct, clothing 57 pct and rubber 35 pct.  \""
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.loc[\"test/14832\"][\"clean_text\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spacy import displacy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  \n",
+       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    Thailand\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+       "</mark>\n",
+       "'s trade deficit widened to \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    4.5  billion baht\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       " in \n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    the first quarter of 1987\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       " from \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    2.1 billion\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       " \n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    a  year ago\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       ", \n",
+       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    the Business Economics Department\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+       "</mark>\n",
+       " said.      It said \n",
+       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    Janunary\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+       "</mark>\n",
+       "/\n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    March\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       " imports rose to \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    65.1 billion baht\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       "  from \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    58.7 billion\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       ". \n",
+       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    Thailand\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+       "</mark>\n",
+       "'s improved business climate \n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    this  year\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       " resulted in a \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    27 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       " increase in imports of raw materials  and semi-finished products.      The country's oil import bill, however, fell \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    23 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       " in \n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    the  first quarter\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       " due to lower oil prices.      The department said \n",
+       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    first quarter\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+       "</mark>\n",
+       " exports expanded to \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    60.6  billion baht\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       " from \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    56.6 billion\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">MONEY</span>\n",
+       "</mark>\n",
+       ".      Export growth was smaller than expected due to lower  earnings from many key commodities including rice whose  earnings declined \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    18 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ", maize \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    66 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ", sugar \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    45 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ", tin \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    26  pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       " and canned pineapples \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    seven pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ".      Products registering high export growth were jewellery up  \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    64 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ", clothing \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    57 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       " and rubber \n",
+       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+       "    35 pct\n",
+       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
+       "</mark>\n",
+       ".  </div></span>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "displacy.render(corpus.loc[\"test/14832\"][\"parsed\"], style='ent', jupyter=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Export corpus Dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \\\n",
+       "id                                                                    \n",
+       "test/14826                                         [trade]       en   \n",
+       "test/14828                                         [grain]       en   \n",
+       "test/14829                                [crude, nat-gas]       en   \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
+       "test/14833                             [palm-oil, veg-oil]       en   \n",
+       "\n",
+       "                                                       parsed  \n",
+       "id                                                             \n",
+       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...  \n",
+       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...  \n",
+       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...  \n",
+       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...  \n",
+       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[[\"clean_text\", \"label\", \"language\", \"parsed\"]].to_pickle(\"corpus.p\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graph Generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following, we will show you how to create two different kind of graphs out of a corpus of documents:\n",
+    "\n",
+    "* Knowledge base graphs, where the subject-verb-object relation will be encoded to build a semantic graph \n",
+    "* Bipartite graphs, linking documents with the entities/keywords appearing therein"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Knowledge base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "corpus = pd.read_pickle(\"corpus.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from subject_object_extraction import findSVOs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[\"triplets\"] = corpus[\"parsed\"].apply(lambda x: findSVOs(x, output=\"obj\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "      <th>triplets</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
+       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
+       "      <td>[(STOCKS, (showed, False), consume), (paper, (...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
+       "      <td>[(Ministry, (revise, False), outlook), (MITI, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
+       "      <td>[(Products, (registering, False), growth), (re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
+       "      <td>[(oil, (told, False), reporters), (Prices, (ar...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \\\n",
+       "id                                                                    \n",
+       "test/14826                                         [trade]       en   \n",
+       "test/14828                                         [grain]       en   \n",
+       "test/14829                                [crude, nat-gas]       en   \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
+       "test/14833                             [palm-oil, veg-oil]       en   \n",
+       "\n",
+       "                                                       parsed  \\\n",
+       "id                                                              \n",
+       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
+       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
+       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
+       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
+       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
+       "\n",
+       "                                                     triplets  \n",
+       "id                                                             \n",
+       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...  \n",
+       "test/14828  [(STOCKS, (showed, False), consume), (paper, (...  \n",
+       "test/14829  [(Ministry, (revise, False), outlook), (MITI, ...  \n",
+       "test/14832  [(Products, (registering, False), growth), (re...  \n",
+       "test/14833  [(oil, (told, False), reporters), (Prices, (ar...  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_list = [\n",
+    "    {\"id\": _id, \"source\": source.lemma_.lower(), \"target\": target.lemma_.lower(), \"edge\": edge.lemma_.lower()}\n",
+    "    for _id, triplets in corpus[\"triplets\"].items()\n",
+    "    for (source, (edge, neg), target) in triplets\n",
+    "    if not any([source.is_stop, target.is_stop])\n",
+    "    if (source.pos_ == \"PROPN\" or source.pos_ == \"NOUN\") and (target.pos_== \"PROPN\" or target.pos_== \"NOUN\") \n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37729"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(edge_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = pd.DataFrame(edge_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "source\n",
+       "company       941\n",
+       "bank          781\n",
+       "net           684\n",
+       "government    422\n",
+       "agreement     418\n",
+       "board         398\n",
+       "plan          374\n",
+       "inc           333\n",
+       "group         308\n",
+       "japan         280\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges[\"source\"].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "edge\n",
+       "be         2651\n",
+       "include    1459\n",
+       "have       1386\n",
+       "tell       1118\n",
+       "buy         715\n",
+       "take        634\n",
+       "sell        563\n",
+       "make        556\n",
+       "give        522\n",
+       "exclude     475\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges[\"edge\"].value_counts().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G=nx.from_pandas_edgelist(edges, \"source\", \"target\", \n",
+    "                          edge_attr=True, create_using=nx.MultiDiGraph())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6112"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(G.nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotDistribution(serie: pd.Series, nbins: int, minValue=None, maxValue=None):\n",
+    "    _minValue=int(np.floor(np.log10(minValue if minValue is not None else serie.min())))\n",
+    "    _maxValue=int(np.ceil(np.log10(maxValue if maxValue is not None else serie.max())))\n",
+    "    bins = [0] + list(np.logspace(_minValue, _maxValue, nbins)) + [np.inf]\n",
+    "    serie.hist(bins=bins)\n",
+    "    plt.xscale(\"log\")\n",
+    "    plt.xlabel(f\"log_10({serie.name})\")\n",
+    "    plt.ylabel(\"Frequency\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def graphSummary(graph, bins=10, plot_edge_weight=False, use_log_y=True):\n",
+    "    print(nx.info(graph))\n",
+    "    plt.figure(figsize=(14 if plot_edge_weight else 6, 5 if plot_edge_weight else 4))\n",
+    "    if plot_edge_weight:\n",
+    "        plt.subplot(1,2,1)\n",
+    "    degrees = pd.Series({k: v for k, v in nx.degree(graph)}, name=\"degree\")\n",
+    "    if use_log_y:\n",
+    "        plt.yscale(\"log\")\n",
+    "    plotDistribution(degrees, bins)\n",
+    "\n",
+    "    if plot_edge_weight:\n",
+    "        plt.subplot(1,2,2)\n",
+    "        allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in graph.edges(data=True)}, name=\"edge_weights\")\n",
+    "        plotDistribution(allEdgesWeights, bins)\n",
+    "        if use_log_y:\n",
+    "            plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: MultiDiGraph\n",
+      "Number of nodes: 6112\n",
+      "Number of edges: 37729\n",
+      "Average in degree:   6.1729\n",
+      "Average out degree:   6.1729\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(G))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: MultiDiGraph\n",
+      "Number of nodes: 6112\n",
+      "Number of edges: 37729\n",
+      "Average in degree:   6.1729\n",
+      "Average out degree:   6.1729\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graphSummary(G, bins=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis of a certain relation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>source</th>\n",
+       "      <th>target</th>\n",
+       "      <th>edge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>test/14826</td>\n",
+       "      <td>exporter</td>\n",
+       "      <td>damage</td>\n",
+       "      <td>fear</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>test/14826</td>\n",
+       "      <td>japan</td>\n",
+       "      <td>fear</td>\n",
+       "      <td>raise</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>test/14826</td>\n",
+       "      <td>row</td>\n",
+       "      <td>damage</td>\n",
+       "      <td>inflict</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>test/14826</td>\n",
+       "      <td>loss</td>\n",
+       "      <td>gain</td>\n",
+       "      <td>be</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>test/14826</td>\n",
+       "      <td>pact</td>\n",
+       "      <td>semiconductor</td>\n",
+       "      <td>sell</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id    source         target     edge\n",
+       "0  test/14826  exporter         damage     fear\n",
+       "1  test/14826     japan           fear    raise\n",
+       "2  test/14826       row         damage  inflict\n",
+       "3  test/14826      loss           gain       be\n",
+       "4  test/14826      pact  semiconductor     sell"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e = edges[(edges[\"source\"]!=\" \") & (edges[\"target\"]!=\" \") & (edges[\"edge\"]==\"lend\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G=nx.from_pandas_edgelist(e, \"source\", \"target\", \n",
+    "                          edge_attr=True, create_using=nx.MultiDiGraph())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "plt.figure(figsize=(9, 5))\n",
+    "\n",
+    "pos = nx.fruchterman_reingold_layout(G, k=1.6) # k regulates the distance between nodes\n",
+    "\n",
+    "nx.draw(G, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos, font_size=12)\n",
+    "\n",
+    "# plt.show()\n",
+    "# plt.savefig(os.path.join(\".\", \"KnowledgeGraph.png\"), dpi=300, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bipartite Graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by extracting the keywords from the documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gensim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.summarization import keywords "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('trading', 0.46151306395385305),\n",
+       " ('said', 0.3159855693494513),\n",
+       " ('export', 0.2691553824958075),\n",
+       " ('import', 0.17462010006456907),\n",
+       " ('japanese electronics', 0.13609326263790283),\n",
+       " ('industry', 0.12860437403797767),\n",
+       " ('minister', 0.12229815662000476),\n",
+       " ('japan', 0.11434500812642369),\n",
+       " ('year', 0.1048399240935248)]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text = corpus[\"clean_text\"][0]\n",
+    "keywords(text, words=10, split=True, scores=True, pos_filter=('NN', 'JJ'), lemmatize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus[\"keywords\"] = corpus[\"clean_text\"].apply(\n",
+    "    lambda text: keywords(text, words=10, split=True, scores=True, pos_filter=('NN', 'JJ'), lemmatize=True)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "      <th>triplets</th>\n",
+       "      <th>keywords</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
+       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
+       "      <td>[(trading, 0.4615130639538536), (said, 0.31598...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
+       "      <td>[(STOCKS, (showed, False), consume), (paper, (...</td>\n",
+       "      <td>[(vermin, 0.31206143802871755), (daily, 0.2611...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
+       "      <td>[(Ministry, (revise, False), outlook), (MITI, ...</td>\n",
+       "      <td>[(energy, 0.38576360926601216), (demand, 0.347...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
+       "      <td>[(Products, (registering, False), growth), (re...</td>\n",
+       "      <td>[(pct, 0.5457455609144314), (export, 0.2656069...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
+       "      <td>[(oil, (told, False), reporters), (Prices, (ar...</td>\n",
+       "      <td>[(indonesia, 0.24104282355029413), (harahap, 0...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \\\n",
+       "id                                                                    \n",
+       "test/14826                                         [trade]       en   \n",
+       "test/14828                                         [grain]       en   \n",
+       "test/14829                                [crude, nat-gas]       en   \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
+       "test/14833                             [palm-oil, veg-oil]       en   \n",
+       "\n",
+       "                                                       parsed  \\\n",
+       "id                                                              \n",
+       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
+       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
+       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
+       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
+       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
+       "\n",
+       "                                                     triplets  \\\n",
+       "id                                                              \n",
+       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...   \n",
+       "test/14828  [(STOCKS, (showed, False), consume), (paper, (...   \n",
+       "test/14829  [(Ministry, (revise, False), outlook), (MITI, ...   \n",
+       "test/14832  [(Products, (registering, False), growth), (re...   \n",
+       "test/14833  [(oil, (told, False), reporters), (Prices, (ar...   \n",
+       "\n",
+       "                                                     keywords  \n",
+       "id                                                             \n",
+       "test/14826  [(trading, 0.4615130639538536), (said, 0.31598...  \n",
+       "test/14828  [(vermin, 0.31206143802871755), (daily, 0.2611...  \n",
+       "test/14829  [(energy, 0.38576360926601216), (demand, 0.347...  \n",
+       "test/14832  [(pct, 0.5457455609144314), (export, 0.2656069...  \n",
+       "test/14833  [(indonesia, 0.24104282355029413), (harahap, 0...  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extractEntities(ents, minValue=1, typeFilters=[\"GPE\", \"ORG\", \"PERSON\"]):\n",
+    "    entities = pd.DataFrame([\n",
+    "        {\"lemma\": e.lemma_, \"lower\": e.lemma_.lower(), \"type\": e.label_}\n",
+    "        for e in ents if hasattr(e, \"label_\")\n",
+    "    ])\n",
+    "\n",
+    "    if len(entities)==0:\n",
+    "        return pd.DataFrame()\n",
+    "    \n",
+    "    g = entities.groupby([\"type\", \"lower\"])\n",
+    "\n",
+    "    summary = pd.concat({\n",
+    "        \"alias\": g.apply(lambda x: x[\"lemma\"].unique()), \n",
+    "        \"count\": g[\"lower\"].count()\n",
+    "    }, axis=1)\n",
+    "    \n",
+    "    summary = summary[summary[\"count\"]>1]\n",
+    "\n",
+    "    subselection = list(set(summary.index.get_level_values(\"type\")).intersection(typeFilters))\n",
+    "\n",
+    "    return summary.loc[pd.IndexSlice[subselection, :, :]]\n",
+    "    \n",
+    "def getOrEmpty(parsed, _type):\n",
+    "    try:\n",
+    "        return list(parsed.loc[_type][\"count\"].sort_values(ascending=False).to_dict().items())\n",
+    "    except:\n",
+    "        return []\n",
+    "\n",
+    "def toField(ents):\n",
+    "    typeFilters=[\"GPE\", \"ORG\", \"PERSON\"]\n",
+    "    parsed = extractEntities(ents, 1, typeFilters)\n",
+    "    return pd.Series({_type: getOrEmpty(parsed, _type) for _type in typeFilters})\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entities = corpus[\"parsed\"].apply(lambda x: toField(x.ents))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged = pd.concat([corpus, entities], axis=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "      <th>triplets</th>\n",
+       "      <th>keywords</th>\n",
+       "      <th>GPE</th>\n",
+       "      <th>ORG</th>\n",
+       "      <th>PERSON</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
+       "      <td>[(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...</td>\n",
+       "      <td>[(trading, 0.4615130639538536), (said, 0.31598...</td>\n",
+       "      <td>[(u.s., 14), (japan, 12), (taiwan, 3), (austra...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
+       "      <td>[(STOCKS, (showed, False), consume), (paper, (...</td>\n",
+       "      <td>[(vermin, 0.31206143802871755), (daily, 0.2611...</td>\n",
+       "      <td>[(china, 2)]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
+       "      <td>[(Ministry, (revise, False), outlook), (MITI, ...</td>\n",
+       "      <td>[(energy, 0.38576360926601216), (demand, 0.347...</td>\n",
+       "      <td>[(japan, 2)]</td>\n",
+       "      <td>[(miti, 4)]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
+       "      <td>[(Products, (registering, False), growth), (re...</td>\n",
+       "      <td>[(pct, 0.5457455609144314), (export, 0.2656069...</td>\n",
+       "      <td>[(thailand, 2)]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
+       "      <td>[(oil, (told, False), reporters), (Prices, (ar...</td>\n",
+       "      <td>[(indonesia, 0.24104282355029413), (harahap, 0...</td>\n",
+       "      <td>[(indonesia, 4), (malaysia, 2)]</td>\n",
+       "      <td>[(cpo, 3)]</td>\n",
+       "      <td>[(harahap, 2)]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \\\n",
+       "id                                                                    \n",
+       "test/14826                                         [trade]       en   \n",
+       "test/14828                                         [grain]       en   \n",
+       "test/14829                                [crude, nat-gas]       en   \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
+       "test/14833                             [palm-oil, veg-oil]       en   \n",
+       "\n",
+       "                                                       parsed  \\\n",
+       "id                                                              \n",
+       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...   \n",
+       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...   \n",
+       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...   \n",
+       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...   \n",
+       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...   \n",
+       "\n",
+       "                                                     triplets  \\\n",
+       "id                                                              \n",
+       "test/14826  [(EXPORTERS, (FEAR, False), DAMAGE), (Japan, (...   \n",
+       "test/14828  [(STOCKS, (showed, False), consume), (paper, (...   \n",
+       "test/14829  [(Ministry, (revise, False), outlook), (MITI, ...   \n",
+       "test/14832  [(Products, (registering, False), growth), (re...   \n",
+       "test/14833  [(oil, (told, False), reporters), (Prices, (ar...   \n",
+       "\n",
+       "                                                     keywords  \\\n",
+       "id                                                              \n",
+       "test/14826  [(trading, 0.4615130639538536), (said, 0.31598...   \n",
+       "test/14828  [(vermin, 0.31206143802871755), (daily, 0.2611...   \n",
+       "test/14829  [(energy, 0.38576360926601216), (demand, 0.347...   \n",
+       "test/14832  [(pct, 0.5457455609144314), (export, 0.2656069...   \n",
+       "test/14833  [(indonesia, 0.24104282355029413), (harahap, 0...   \n",
+       "\n",
+       "                                                          GPE          ORG  \\\n",
+       "id                                                                           \n",
+       "test/14826  [(u.s., 14), (japan, 12), (taiwan, 3), (austra...           []   \n",
+       "test/14828                                       [(china, 2)]           []   \n",
+       "test/14829                                       [(japan, 2)]  [(miti, 4)]   \n",
+       "test/14832                                    [(thailand, 2)]           []   \n",
+       "test/14833                    [(indonesia, 4), (malaysia, 2)]   [(cpo, 3)]   \n",
+       "\n",
+       "                    PERSON  \n",
+       "id                          \n",
+       "test/14826              []  \n",
+       "test/14828              []  \n",
+       "test/14829              []  \n",
+       "test/14832              []  \n",
+       "test/14833  [(harahap, 2)]  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We finally create the bipartite graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = pd.DataFrame([\n",
+    "    {\"source\": _id, \"target\": keyword, \"weight\": score, \"type\": _type}\n",
+    "    for _id, row in merged.iterrows()\n",
+    "    for _type in [\"keywords\", \"GPE\", \"ORG\", \"PERSON\"] \n",
+    "    for (keyword, score) in row[_type]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = nx.Graph()\n",
+    "G.add_nodes_from(edges[\"source\"].unique(), bipartite=0)\n",
+    "G.add_nodes_from(edges[\"target\"].unique(), bipartite=1)\n",
+    "G.add_edges_from([\n",
+    "    (row[\"source\"], row[\"target\"])\n",
+    "    for _, row in edges.iterrows()\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_nodes = {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 0}\n",
+    "entity_nodes = {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 1}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes_with_low_degree = {n for n, d in nx.degree(G, nbunch=entity_nodes) if d<5}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 25931\n",
+      "Number of edges: 100712\n",
+      "Average degree:   7.7677\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(G))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges.to_pickle(\"bipartiteEdges.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from networkx.algorithms.bipartite.projection import overlap_weighted_projected_graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Entity-Entity Graph Projection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subGraph = G.subgraph(set(G.nodes) - nodes_with_low_degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityGraph = overlap_weighted_projected_graph(\n",
+    "    subGraph, \n",
+    "    {n for n, d in subGraph.nodes(data=True) if d[\"bipartite\"] == 1}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2383"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(entityGraph.nodes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 2383\n",
+      "Number of edges: 120596\n",
+      "Average degree: 101.2136\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n",
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graphSummary(entityGraph, 100, plot_edge_weight=True, use_log_y=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degrees = pd.Series({k: v for k, v in nx.degree(entityGraph)}, name=\"degree\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filteredEntityGraph = entityGraph.edge_subgraph(\n",
+    "    [edge for edge in entityGraph.edges if entityGraph.edges[edge][\"weight\"]>0.05]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 2267\n",
+      "Number of edges: 8111\n",
+      "Average degree:   7.1557\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(filteredEntityGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 2267\n",
+      "Number of edges: 8111\n",
+      "Average degree:   7.1557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.01, 8)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graphSummary(filteredEntityGraph, 13, plot_edge_weight=True, use_log_y=False)\n",
+    "plt.xlim([0.01,8])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Local and global properties of the graph "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "globalKpis = [{\n",
+    "    \"shortest_path\": nx.average_shortest_path_length(_graph),\n",
+    "    \"clustering_coefficient\": nx.average_clustering(_graph),\n",
+    "    \"global_efficiency\": nx.global_efficiency(_graph)\n",
+    "} for components in nx.connected_components(filteredEntityGraph) \n",
+    "    for _graph in [nx.subgraph(filteredEntityGraph, components)]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>shortest_path</th>\n",
+       "      <th>clustering_coefficient</th>\n",
+       "      <th>global_efficiency</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.722114</td>\n",
+       "      <td>0.21808</td>\n",
+       "      <td>0.227060</td>\n",
+       "      <td>2251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.333333</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   shortest_path  clustering_coefficient  global_efficiency     0\n",
+       "0       4.722114                 0.21808           0.227060  2251\n",
+       "1       1.000000                 0.00000           1.000000     2\n",
+       "2       1.600000                 0.00000           0.700000     5\n",
+       "3       1.000000                 0.00000           1.000000     2\n",
+       "4       1.000000                 0.00000           1.000000     2\n",
+       "5       1.333333                 0.00000           0.833333     3\n",
+       "6       1.000000                 0.00000           1.000000     2"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([\n",
+    "    pd.DataFrame(globalKpis), \n",
+    "    pd.Series([len(c) for c in nx.connected_components(filteredEntityGraph)])\n",
+    "], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2267"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series([len(c) for c in nx.connected_components(filteredEntityGraph)]).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shortest_path': 4.722114220840121,\n",
+       " 'clustering_coefficient': 0.21807986369292282,\n",
+       " 'global_efficiency': 0.22705958936010567}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "globalKpis[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# nx.write_gexf(filteredEntityGraph, \"filteredEntityGraph.gexf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "betweeness = nx.betweenness_centrality(filteredEntityGraph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_betweeness = pd.Series(betweeness, name=\"betweeness\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDistribution(_betweeness[_betweeness>0], 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pageRanks = pd.Series(nx.pagerank(filteredEntityGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degrees = pd.Series({k: v for k, v in nx.degree(filteredEntityGraph)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpis = pd.concat({\n",
+    "    \"pageRank\": pageRanks, \n",
+    "    \"degrees\": degrees, \n",
+    "    \"betweeness\": _betweeness\n",
+    "}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "uae             0.000603\n",
+       "spokeswoman     0.000279\n",
+       "compensation    0.000595\n",
+       "wholly          0.000380\n",
+       "brazilian       0.000223\n",
+       "Name: pageRank, dtype: float64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpis[\"pageRank\"].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1e-05, 0.02)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.title(\"Page rank vs degrees\")\n",
+    "plt.plot(kpis[\"pageRank\"].values, kpis[\"degrees\"].values, '.', color=\"tab:blue\")\n",
+    "plt.xlabel(\"page rank\")\n",
+    "plt.ylabel(\"degree\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.title(\"Page rank vs betweeness\")\n",
+    "plt.plot(kpis[\"pageRank\"].values, kpis[\"betweeness\"].values, '.', color=\"tab:blue\")\n",
+    "plt.xlabel(\"page rank\")\n",
+    "plt.ylabel(\"betweeness\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylim([1E-5, 2E-2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualization of the Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create network layout for visualizations\n",
+    "spring_pos = nx.spring_layout(filteredEntityGraph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_edge_color = 'gray'\n",
+    "default_node_color = '#407cc9'\n",
+    "enhanced_node_color = '#f5b042'\n",
+    "enhanced_edge_color = '#cc2f04'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.axis(\"off\")\n",
+    "nx.draw_networkx(filteredEntityGraph, pos=spring_pos, node_color=default_node_color, \n",
+    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Community detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "communities = pd.Series(community.best_partition(filteredEntityGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '# Members')"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9iy++0IgRIxQREaHg4GB17dpVu3btco8bY/Twww/r4osvVnBwsFJTU7Vv37567BgAAAAAAGvYOrT/97//VUpKigICAvSPf/xD//73v/W///u/uuiii9z7PP7445o3b55yc3OVn5+vFi1aKC0tTZWVlfXYOQAAAAAA3rug72mvK3PnzlVMTIyWLFni3hYfH+/+szFGOTk5mjZtmoYMGSJJevHFFxUZGalVq1Zp+PDhdd4zAAAAAABWsfVM+9///nf16NFDN998s9q2bavu3btr8eLF7vGDBw+quLhYqamp7m2hoaFKSkpSXl5efbQMAAAAAIBlbB3aDxw4oIULF6pDhw566623dM8992jChAl64YUXJEnFxcWSpMjISI/HRUZGusfOxeVyqby83GMBAAAAAMBubH15fHV1tXr06KHHHntMktS9e3d99NFHys3N1e23337Bx83OztbMmTOtahMAAMAycVPX1voxh+ak+6ATAIAd2Hqm/eKLL1bnzp09tiUkJOjw4cOSpKioKElSSUmJxz4lJSXusXPJyspSWVmZeykqKrK4cwAAAAAAvGfr0J6SkqK9e/d6bPv0008VGxsr6fRN6aKiorRhwwb3eHl5ufLz85WcnHze4wYGBiokJMRjAQAAAADAbmx9efykSZPUu3dvPfbYY7rlllu0Y8cOLVq0SIsWLZIkORwOTZw4UbNnz1aHDh0UHx+v6dOnKzo6WkOHDq3n7gEAAAAA8I6tQ3vPnj31xhtvKCsrS7NmzVJ8fLxycnKUkZHh3ueBBx7Q8ePHNXbsWJWWlqpPnz5at26dgoKC6rFzAAAAAAC8Z+vQLkm/+c1v9Jvf/Oa84w6HQ7NmzdKsWbPqsCsAAAAAAHzP1p9pBwAAAACgKSO0AwAAAABgU4R2AAAAAABsyvafaQcAAID14qaurdX+h+ak+6gTAMBPYaYdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGzKv74bAAAAQOMUN3VtrR9zaE66DzoBgIaLmXYAAAAAAGyK0A7NrXQAACAASURBVA4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsCn/+m4AAAAA8Ebc1LW1fsyhOem2rQMAP8RMOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsKkGFdrnzJkjh8OhiRMnurdVVlZq3LhxioiIUMuWLTVs2DCVlJTUY5cAAAAAAFijwYT2nTt36i9/+YuuuOIKj+2TJk3SmjVrtHLlSm3ZskVHjhzRjTfeWE9dAgAAAABgnQYR2isqKpSRkaHFixfroosucm8vKyvTc889pyeffFL9+/dXYmKilixZovfee0/bt2+vx44BAAAAAPBegwjt48aNU3p6ulJTUz22FxQU6OTJkx7bO3XqpPbt2ysvL6+u2wQAAAAAwFL+9d3Az3n55ZdVWFionTt3njVWXFysZs2aKSwszGN7ZGSkiouLz3tMl8sll8vlXi8vL7euYQAAAAAALGLrmfaioiLde++9Wr58uYKCgiw7bnZ2tkJDQ91LTEyMZccGAAAAAMAqtg7tBQUFOnr0qH71q1/J399f/v7+2rJli+bNmyd/f39FRkaqqqpKpaWlHo8rKSlRVFTUeY+blZWlsrIy91JUVOTrUwEAAAAAoNZsfXn8tddeqw8//NBj2+jRo9WpUyc9+OCDiomJUUBAgDZs2KBhw4ZJkvbu3avDhw8rOTn5vMcNDAxUYGCgT3sHAAAAAMBbtg7trVq1UpcuXTy2tWjRQhEREe7tY8aM0eTJkxUeHq6QkBCNHz9eycnJuuqqq+qjZQAAAAAALGPr0F4TTz31lJxOp4YNGyaXy6W0tDQ9++yz9d0WAAAAAABea3ChffPmzR7rQUFBWrBggRYsWFA/DQEAAAAA4CO2vhEdAAAAAABNGaEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYFKEdAAAAAACbIrQDAAAAAGBThHYAAAAAAGyK0A4AAAAAgE0R2gEAAAAAsClCOwAAAAAANkVoBwAAAADApgjtAAAAAADYlH99NwAAAADg/8RNXVvrxxyak+6DTgDYATPtAAAAAADYFKEdAAAAAACb4vJ4AAAAoAniMnygYWCmHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKZsH9qzs7PVs2dPtWrVSm3bttXQoUO1d+9ej30qKys1btw4RUREqGXLlho2bJhKSkrqqWMAAAAAAKxh+9C+ZcsWjRs3Ttu3b9f69et18uRJXXfddTp+/Lh7n0mTJmnNmjVauXKltmzZoiNHjujGG2+sx64BAAAAAPCef3038HPWrVvnsb506VK1bdtWBQUFuvrqq1VWVqbnnntOK1asUP/+/SVJS5YsUUJCgrZv366rrrqqPtoGAAAAAMBrtp9p/7GysjJJUnh4uCSpoKBAJ0+eVGpqqnufTp06qX379srLy6uXHgEAAAAAsILtZ9p/qLq6WhMnTlRKSoq6dOkiSSouLlazZs0UFhbmsW9kZKSKi4vPeRyXyyWXy+VeLy8v913TAAAAAABcoAY10z5u3Dh99NFHevnll706TnZ2tkJDQ91LTEyMRR0CAAAAAGCdBhPaMzMz9eabb2rTpk1q166de3tUVJSqqqpUWlrqsX9JSYmioqLOeaysrCyVlZW5l6KiIp/2DgAAAADAhbB9aDfGKDMzU2+88YY2btyo+Ph4j/HExEQFBARow4YN7m179+7V4cOHlZycfM5jBgYGKiQkxGMBAAAAAMBubP+Z9nHjxmnFihVavXq1WrVq5f6cemhoqIKDgxUaGqoxY8Zo8uTJCg8PV0hIiMaPH6/k5GTuHA8AAAAAaNBsH9oXLlwoSerbt6/H9iVLluiOO+6QJD311FNyOp0aNmyYXC6X0tLS9Oyzz9ZxpwAAAAAAWMv2od0Y87P7BAUFacGCBVqwYEEddAQAAACgpuKmrq3V/ofmpPuoE6Bhsv1n2gEAAAAAaKoI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsyr++GwAAAAAAb8RNXVvrxxyak+6DTgDrMdMOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApQjsAAAAAADZFaAcAAAAAwKYI7QAAAAAA2BShHQAAAAAAmyK0AwAAAABgU/713QAAAAAANARxU9fW+jGH5qT7oBM0Jcy0AwAAAABgU4R2AAAAAABsitAOAAAAAIBNEdoBAAAAALApbkQHAAAAADbCDe/wQ8y0AwAAAABgU8y0AwAAAEATVFcz+natY+dz+aFGM9O+YMECxcXFKSgoSElJSdqxY0d9twQAAAAAgFcaRWh/5ZVXNHnyZD3yyCMqLCzUlVdeqbS0NB09erS+WwMAAAAA4II1itD+5JNP6q677tLo0aPVuXNn5ebmqnnz5nr++efruzUAAAAAAC5Yg/9Me1VVlQoKCpSVleXe5nQ6lZqaqry8vHM+xuVyyeVyudfLysokSeXl5WftW+06UeueznWcn0OduqnTmM6FOvau05jOhTr2rtOYzoU69q7TmM6FOvau05jOhTr2rlPf53JmuzHmJx/vMD+3h80dOXJEl1xyid577z0lJye7tz/wwAPasmWL8vPzz3rMjBkzNHPmzLpsEwAAAACAsxQVFaldu3bnHW/wM+0XIisrS5MnT3avV1dX69ixY4qIiJDD4ajRMcrLyxUTE6OioiKFhIT4qtU6qdOYzoU69q7TmM6FOvau05jOhTr2rtOYzoU69q7TmM6FOvau05jOxe51jDH69ttvFR0d/ZP7NfjQ3rp1a/n5+amkpMRje0lJiaKios75mMDAQAUGBnpsCwsLu6D6ISEhPv3Hr8s6jelcqGPvOo3pXKhj7zqN6VyoY+86jelcqGPvOo3pXKhj7zqN6VzsXCc0NPRn92nwN6Jr1qyZEhMTtWHDBve26upqbdiwweNyeQAAAAAAGpoGP9MuSZMnT9btt9+uHj16qFevXsrJydHx48c1evTo+m4NAAAAAIAL5jdjxowZ9d2Et7p06aKwsDA9+uijeuKJJyRJy5cvV8eOHX1a18/PT3379pW/v2/f+6iLOo3pXKhj7zqN6VyoY+86jelcqGPvOo3pXKhj7zqN6VyoY+86jelcGkOdBn/3eAAAAAAAGqsG/5l2AAAAAAAaK0I7AAAAAAA2RWgHAAAAAMCmCO2oU9xCAQAAAABqrlF85RsajsDAQL3//vtKSEio71Zq7euvv9bzzz+vvLw8FRcXS5KioqLUu3dv3XHHHWrTpk09dwgAAACgseHu8U3Qd999p4KCAoWHh6tz584eY5WVlXr11Vc1atQor2pMnjz5nNuffvppjRgxQhEREZKkJ5980qs648eP1y233KJf//rXXh3n5+zcuVNpaWlq3ry5UlNTFRkZKUkqKSnRhg0bdOLECb311lvq0aOHT/vAuX355ZdauHChtm3bpi+//FJOp1OXXnqphg4dqjvuuEN+fn6W1dq4ceNZdQYPHqwOHTpYVgMXprq6Wk7n2ReQVVdX6z//+Y/at29fD101PP3799eSJUsUGxtr2TH37Nmj7du3Kzk5WZ06ddInn3yip59+Wi6XSyNGjFD//v29rvHaa69pwIABat68uQUd18zx48f16quvav/+/br44ot12223uX+/NVQHDx50n0+XLl3qux1L/fe//9WaNWu8fo2D2nO5XHI6nQoICJAkffbZZ3r++ed1+PBhxcbGasyYMYqPj7es3o4dO86aZElOTlavXr0sq9GYGGN06NAhxcTEyN/fX1VVVXrjjTfkcrk0cOBAtW7dur5btKX3339fBQUF6tu3ry699FJ9/PHHWrBggaqrq3XDDTcoLS3NumIGXjt8+LAZPXq018cpKCgwBw4ccK+/+OKLpnfv3qZdu3YmJSXFvPTSS17X2Lt3r4mNjTUOh8M4nU5z9dVXmyNHjrjHi4uLjdPp9LqOw+Ew3bp1M3379vVYHA6H6dmzp+nbt6/p16+fJXWcTqfp0KGDmTNnjvnyyy+9Pua5JCUlmbFjx5rq6uqzxqqrq83YsWPNVVddZUmtNWvWmOnTp5tt27YZY4zZsGGDGTBggElLSzN/+ctfLKlhjDEul8u88sorZuLEiWb48OFm+PDhZuLEiebVV181LpfLkhpFRUXmq6++cq9v3brV/Pa3vzV9+vQxGRkZ5r333vO6xs6dO01oaKhJTEw0ffr0MX5+fmbkyJHm1ltvNWFhYaZ3796mvLzc6zolJSWmV69exul0Gn9/f+N0Ok1iYqKJiooyfn5+ZsqUKV7X+KGioiLz7bffnrW9qqrKbNmyxZIa8+fPNyNHjnQ/t7z44osmISHBdOzY0WRlZZmTJ09aUufrr782GzduNN98840xxpivvvrKzJkzx8ycOdP8+9//9vr4ZWVl5uabbzZBQUGmbdu2Zvr06eb77793j1v1vFYTxcXFZubMmV4f54knnjCHDh2yoKPzW7169TkXPz8/88wzz7jXvfWPf/zDNGvWzISHh5ugoCDzj3/8w7Rp08akpqaa/v37Gz8/P7Nhwwav6zgcDhMSEmLuuusus337dq+Pdy4JCQnun+PDhw+buLg4Exoaanr27GnCw8NN27ZtPX6Pe+vEiRPmueeeM6NHjzbXX3+9GThwoMnMzDTvvPOOJce/55573M8zJ06cMMOGDTNOp9P9u7Vfv37nfB6y2rFjx8wLL7zg8zq7d+/26XNBfHy8+fTTT312/OrqarNx40azaNEis2bNGlNVVWXZsfPz801OTo6ZOnWqmTp1qsnJyTH5+fmWHf+aa64xK1euNMYYs23bNhMYGGiuuOIKc+utt5ru3bub5s2bW/KaoKSkxPTp08c4HA4TGxtrevXqZXr16uV+7dunTx9TUlLidZ0zTp06dd7tn3/+uWV1fujAgQPm7bffNh9++KElx/vkk09MbGyscTqd5rLLLjMHDhwwiYmJpkWLFqZ58+amdevWlv1cV1dXmwMHDrhfX7hcLvPyyy+bF154weP1oi/069fP0t+rr732mvHz8zMRERGmZcuWZv369SYsLMykpqaatLQ04+fnZ5YvX25ZPUK7Baz6JXDFFVeY9evXG2OMWbx4sQkODjYTJkwwCxcuNBMnTjQtW7Y0zz33nFc1hg4datLT081XX31l9u3bZ9LT0018fLz7icWqF7fZ2dkmPj7+rBdi/v7+5uOPP/b6+Gc4HA7zzjvvmHvvvde0bt3aBAQEmMGDB5s1a9ac94n0QgQFBZk9e/acd3zPnj0mKCjI6zq5ubnG39/fJCYmmpCQELNs2TLTqlUrc+edd5q7777bBAcHm5ycHK/r7Nu3z1x66aUmKCjIXHPNNeaWW24xt9xyi7nmmmtMUFCQueyyy8y+ffu8rtOrVy+zZs0aY4wxq1atMk6n0wwePNg8+OCD5oYbbjABAQHu8QuVkpJiZsyY4V5ftmyZSUpKMsacfiHYrVs3M2HCBK9qGGPMrbfeaoYOHWrKyspMZWWlyczMNKNGjTLGnH5jJSIiwpJ/myNHjpiePXsap9PpfgPihy+arfo/+qc//cm0atXKDBs2zERFRZk5c+aYiIgIM3v2bPPYY4+ZNm3amIcfftjrOvn5+SY0NNQ4HA5z0UUXmV27dpn4+HjToUMH84tf/MIEBwebgoICr2pMmDDBXH755WblypVm8eLFJjY21qSnp7vffCouLjYOh8Prc6kJq34fOBwO4+fnZ1JTU83LL79s2RtpP65xJqCdb7HiXJKTk81DDz1kjDHmpZdeMhdddJH54x//6B6fOnWq+Z//+R+v6zgcDjNr1izTvXt343A4zC9/+Uvz1FNPma+//trrY/+wxpkX/BkZGaZ3796mtLTUGGPMt99+a1JTU81tt91mSa19+/aZ2NhY07ZtWxMTE2McDodJT083SUlJxs/Pz9x8881ev7HmdDrd55OVlWXatWtnNm7caI4fP262bdtmfvGLX5ipU6dacTo/yar/N2VlZT+5vPvuu5bUefrpp8+5+Pn5maysLPe6twYMGOD++frmm29MUlKScTgcpk2bNsbpdJpOnTqZo0ePelWjrkJuSEiIO/hdc801ZtKkSR7j06ZNMykpKV7XGTZsmElOTjaffPLJWWOffPKJ6d27t7npppu8rlNXbxbXxRtrQ4YMMYMHDzYffPCBmThxoklISDBDhgwxVVVVprKy0gwaNMiMGDHC63OpqzcH6uoN6V/96ldm9uzZxpjTv9vCwsLMrFmz3ONPPPGE6datm9d1ziC018D5/vHPLE899ZQl/zGDg4Pd7wB1797dLFq0yGN8+fLlpnPnzl7VaNu2rfnggw/c69XV1eb3v/+9ad++vfnss88snZHasWOHufzyy819993nfjfYF6H9zC+Tqqoq88orr7jf3YqOjjZ//OMfLQmfcXFxPzkL8MILL5jY2Fiv63Tu3Nn9775x40YTFBRkFixY4B5fsmSJSUhI8LpOamqqGTJkiCkrKztrrKyszAwZMsRcd911Xtdp0aKFe9YpKSnJzJkzx2N8/vz5pnv37l7VCA4ONp999pl7/dSpUyYgIMAUFxcbY4x5++23TXR0tFc1jDn9guOjjz5yr1dUVJiAgAD33+GyZctMx44dva4zatQok5SUZHbu3GnWr19vEhMTTY8ePcyxY8eMMdYF0F/84hfmtddeM8acfsHs5+dn/vrXv7rHX3/9dXPZZZd5XSc1NdXceeedpry83Pz5z3827dq1M3feead7fPTo0Wbo0KFe1Wjfvr3ZtGmTe/2rr74yvXr1Mtddd52prKy09Hnt/fff/8nllVdesSy0L1myxAwZMsQEBASYiIgIc++991o2s2KMMddff71JT08/6wW51c/TISEh7ufhU6dOGX9/f1NYWOge//DDD01kZKTXdX74+2DXrl3mnnvuMWFhYSYwMNDcfPPN5u2337a0xqWXXnrWMf/5z3+amJgYr+sYczqw3X333e4rvObMmWMGDBhgjDHm008/NXFxceaRRx7xqsYPz6dLly5mxYoVHuOrV682l19+uVc1jKm7MH0myJxvseqNKIfDYdq1a2fi4uI8FofDYS655BITFxdn4uPjLalz5t/nnnvuMZ07d3b/Ti0qKjKJiYnm97//vVc16irktmjRwj35ERkZaXbv3u0xvn//ftOyZUuv67Rs2dLj+eXHdu3aZUmdunqzuC7eWGvTpo3517/+ZYw5/drG4XCYd9991z3+z3/+07Rv396rGsbU3ZsDdfWGdIsWLczBgweNMafzVEBAgEfG+uyzzyz5WTuD0F4DdfWPHxERYXbt2mWMOR2uz/WEFhwc7FWNVq1anfNy1HHjxpl27dqZrVu3Wnrp2LfffmtGjRplrrjiCvPhhx+agIAAn4X2H/r888/NI4884n5Hz1vPPPOMCQwMNBMmTDCrV68227dvN9u3bzerV682EyZMMMHBwR7h+kIFBwd7XE4VEBDg8SL94MGDpnnz5pbU+akX/x988IHXP2vGGBMaGmref/99Y8zpn+kzfz5j//79Xp9PbGys+6MExpyeqXY4HObEiRPGmNN/Z1ZcBdGmTRuPn90TJ04Yp9PpvlT2s88+M4GBgV7XiY6O9rgk8cwvsm7duplvvvnGsgB6rp+1H74pcejQIUt+1i666CL3c05VVZVxOp0e51dQUGAuueQSr2oEBwefdUlyeXm5SU5ONv379zcHDhyw7Hntp34fWB0Kzjy3lZSUmLlz55pOnToZp9NpevbsaRYtWmTJxz6efPJJExMT43HFiy9C+/79+93rLVu29Hij7dChQ5b8Hz3X74PvvvvOvPjii6Zv377G6XSauLg4r2ucmdmMjo4+63nUqnMxxpjmzZt7zDq5XC4TEBDgvnJg1apVlp5P69atPZ4DjDl9Plb8LqirMB0SEmLmzp1rNm/efM5l8eLFltS5++67Tbdu3c56PeXLiYmOHTueNTv4zjvveP3mQF2F3P79+5vHH3/cGGNM7969z5oI+dvf/mZJMIyIiDCbN28+7/imTZtMRESE13Xq6s3iunhj7cevB1q2bOnxnH348GFLXt/U1ZsDdfWGdFRUlDu3HTt2zDgcDo+fiR07dpioqCjL6hHaayA6OtqsWrXqvOP/+te/LPmPOWLECDNmzBhjjDE333yzmTbt/7d358E1nf8fwN/nJiKahMQSrkpkaYhItEaJoEkYmlZFqBlFTcQulkq1tg6ljahlxh+WKoYoJWilBE2MJiJj7DGWjtBIpIKEThGxhDT38/vD5P7cZkHuycnN/b5fM/nj3ufmfM7ynOXznOc8Z55J+eLFiyUgIMCsGF27dpUtW7ZUWjZlyhRxdnaulee9EhISpGXLlqLT6TRJ2ssZDAZV7q6IiOzYsUMCAwPF1tbWeJFua2srgYGBsnPnTlVilDeciIjcvHlTFEWRAwcOGMvT09OlTZs2ZsfR6/XVdktPSkoSvV5vdpyBAwcaW4DDwsIqdBfcsGGD+Pj4mBVj+vTp4u/vL8nJyZKWlia9e/eW0NBQY3lKSop4e3ubFUNEZPDgwTJkyBB5+PChPHv2TGJiYkzuRJ84cUKVg7ODg0OF7mGlpaUyaNAg6dSpk1y4cEGVfdTT01OSk5NF5PldO51OJ7t27TKWHzhwwOyEQMS0JVqkYsL2119/mZ3ktG/f3mQ/KVdcXCxBQUHy9ttvq3Zca9asmWzcuFHy8vIq/Ttw4IDqF2ovysjIkFGjRomDg4M4ODiYHUfk+TnMz89PJkyYII8ePVL9wqZTp07Guiby/M76i926MzIyVLkr+eIdqcpkZ2ebdMuvCUVRJCAgQDp37iyOjo7yyy+/mJQfOXLE7Eaocq1btzZ5dOTevXuiKIqxsSY3N9fsC2lFUWTixIny+eefi6ura4XzZWZmpjRv3tysGCLaJdOhoaGydOnSKsvPnTun2qMyiYmJ4ubmJqtWrTJ+VxtJe3mjiqura6WNKubWAa2S3GPHjkmTJk1kwYIFsmrVKmnevLnMmzdPtm3bJl9//bU4OztXu+1e1eTJk6Vt27aSmJho0puwqKhIEhMTxcPDQ6ZOnWp2HK0ai7VoWPP29jZJnr///nuTRuHMzExVrm+0ahwQ0aZBeuTIkRIYGCg//fSThIeHS1hYmHTv3l2ysrLk8uXLEhISokovlXJM2l9BeHi4zJ8/v8pytU4CN2/eFA8PDwkODpYZM2ZIo0aNpFevXjJ+/HgJDg4WOzu7Si9MX8fixYuN3esqEx0dXWvPfubn58uePXvk4cOHqk3Tw8ND1ecVX8WzZ8/k1q1bcuvWLVUHgRF53nDi4+MjixYtkm7dusmoUaPE19dXkpOTJSUlRQICAmTMmDFmx5k/f764uLjIihUr5Pz581JYWCiFhYVy/vx5WbFihTRt2tTsbpciIpcuXZJmzZpJZGSkxMbGiqOjo4wcOVLi4uIkMjJSGjZsKPHx8WbFKC4ulqFDhxobU3r06GFyIj148KBJMlpTOTk54u3tLba2ttKgQQNxdnY2jkEh8vzRBTWe/QwICKiQCIj8f+Lu7u6uyoXAvHnzpEWLFjJu3Djx9PSUOXPmiLu7u6xdu1Z++OEHcXNzq/DMYU34+vqajG2xf/9+Yy8IkeeNHeY2RE2bNq3KE+ODBw8kMDBQtaT9/fffl9jY2CrL1TofvCwBLSoqqvAIlTkeP34sEydOFB8fH7GxsVH1wmbt2rWyf//+Ksvnzp1rbLA2x8sacdWwcOFCk7+UlBST8i+//FKGDRumSqxRo0ZJSEiIZGVlSW5urnHArnLp6elmd8UPCQkxGSx2w4YNJuWxsbESEhJiVgwR7ZLp9evXV/sseWFhockYKOa6ceOG9OnTRz744AMpKCiolaS9f//+MnjwYHFxcanQ2H7ixAmzHy3RKskVeZ64d+/evUIvpTfffFOVMWFEnvdOmzRpktjZ2YlOpxN7e3uxt7cXnU4ndnZ2Eh0dLSUlJWbH0aqxWIuGtYkTJ1bY91/03XffSf/+/c2KIaJd40C52m6QLiwslH79+omjo6OEhYXJ/fv3ZerUqSaDZL/YKGEuJu2vICMjw+QuwX89fPiw2lbK13Hv3j2ZPXu2+Pn5ib29vdjZ2Unbtm1lxIgRcvr0aVVikOV6+PChjB8/Xvz9/WXChAny9OlTWb58udjZ2YmiKBIaGqraRemSJUtEr9ebdFtUFEX0er0qrd3lrl69KsOGDRMnJyfjCbpBgwbSo0cP+fXXX1WL8+TJk1of5fjRo0dy8OBB2bdvX62Ncjpr1qwqxxMoLS2VgQMHqnJxW1ZWJnFxcTJgwABZvHixGAwGSUhIEDc3N2nWrJlERUWp0sC2cOHCat988dVXX8nHH39sVoy7d+9WuPvwogcPHqh2jE5MTJStW7dWOy+bN282O44WCWhl9u7dKzExMXUS21x5eXmVvuGjvrp9+7YxwdHpdNK2bVuTbsw///yzrFy5slbnQ9k49wAAC2BJREFUIScnR/Lz882ezvr166tNytROprVkMBhk8eLFxjeJqJkUREVFmfz9t1ffzJkzJSwszKwYWiW5L7pz546cOHFCjh07ZtITS01FRUWSlpYm27dvl+3bt0taWlql4/jUlFaNxVo1rFUnNzfX5E1TNaVV48CLarNBuio5OTkVepSpge9pJ6oHSkpKUFpaCicnJ9Wnfe3aNZP3mKr5ntQXiQju3LkDg8GA5s2bG9/VSqb+/fdfPH78GI0bN66y/ObNm6q+Q7suPX78GDY2NmjYsGFdzwqRRcrOzsbTp0/h6+sLW1vbup4dqkJmZiaOHj2KyMhIuLi4aBLz0aNHsLGxgb29vdnTevDgATIzM02uB7p06VLlueh/3b1793Dr1i107Nix0vLi4mKcPXsWISEhtTofubm5sLOzQ5s2bWo1jhauXbsGe3t76PV61aedlJSEw4cPY+7cuXB1dVV9+lrQ1fUMENHL2dvbw8nJCfn5+RgzZoyq0/b09ERQUBCCgoKMCXttxFEUBS1btoRerzcm7LURpzY9efIER48exaVLlyqUlZSUYMuWLWbHsLW1rfYiqaCgAN98843ZcSzFP//8g+joaLOno8W2eRVa1Wm14ljKelOLtS0PAPj4+MDf379Cwl7f6kBWVhbi4+Nx+fJlAMDly5cRHR2NMWPGIC0tTZUYQN3VgS5dumD69OlwcXHR7Dhw9+5dTJ482ezpZGVlYffu3dDr9Rg+fDg6d+6MXbt2ISYmpl5uGy3iuLi4QKfTVVmnT58+rVrCXr7vXLlypUKcvLw8VRJ2SzgOXLt2TbWE/b9x2rVrhydPnmDOnDn1sk4DALvHE9Ujar3L9n8tjhquXLlifG+tTqeT4OBgk+5iar5WrDr1aZ29CjWWx1K2jUj92ncsab2pwdqW52XqUx1ITk4WOzs7adq0qdjb20tycrK0aNFC+vbtK3369BEbGxuTsS9qylLqQH06Dljbtqkszs2bN1WPo9V60yKOtR0HrK1Ol2M/KyILkpSUVG15bm4u49SR2bNnw9/fH2fOnMH9+/cRExODnj17Ij09He7u7qrFsaZ1BmizPFptG8C69h0t15sWrG15rKkOfPvtt5g5cyYWLVqEHTt2YMSIEYiOjkZcXBwAYO7cuViyZAn69OljVhxrO05rEcfatk1lcXr16lVv67QWcaztOGBtddpItfSfiMxW3TugX3wXNONoz9XVVS5cuGD8bDAYZNKkSeLu7i45OTmqvpPVWtaZiDbLo9W2EbGufUfL9aYFa1sea6oDjRs3luzsbBF5Pgimra2tyaB6Fy9eNHsUdBHrO05rEcfato211Wkt4nCd1YzW5xw+005kQfR6PRITE2EwGCr9O3v2LOPUkSdPnpg8U6ooCtauXYvw8HCEhITgzz//VCWONa0zQJvl0WrbANa172i53rRgbctjbXVAURQAgE6ng729PZo0aWIsc3JyQlFRkdkxrO04rVUca9o21lantYjDdVYzWp9zmLQTWZAuXbogMzOzynJFUSAqvPDB2uJowdfXF2fOnKnw/erVqxEREYGBAweqEsea1hmgzfJotW0A69p3tFxvWrC25bGmOuDh4YHs7Gzj5+PHj5t0H71+/boqA1BZ23FaizjWtm2srU5rEYfrrGa0PucwaSeyIDNnzkSPHj2qLH/rrbdw+PBhxqkDgwcPRkJCQqVlq1evxvDhw1W5SLOmdQZoszxabRvAuvYdLdebFqxteaypDkRHR6OsrMz4+b+j4ScnJ5v9fClgfcdpLeJY27axtjqtRRyus5rR+pzD97QTERERERERWSjeaSciIiIiIiKyUEzaiYiIiIiIiCwUk3YiIiIiIiIiC8WknYiIiIiIiMhCMWknIiIii6UoCvbs2VPXs0FERFRnmLQTERHVE4WFhZg2bRq8vLzQsGFDuLm5ITw8HKmpqXU9a7WmoKAAH374IQAgLy8PiqLg3LlzdTxXRERE2rF9+U+IiIioruXl5aFnz55wdnbG8uXLERAQgNLSUhw8eBBTpkzB5cuX63oWa0WrVq3qehaIiIjqFO+0ExER1QOTJ0+Goig4deoUhgwZgnbt2qFjx46YMWMGTpw4AQC4fv06IiIi4OjoiMaNG2Po0KG4ffu2cRoLFy7EO++8g02bNsHd3R2Ojo6YPHkyysrKsGzZMrRq1Qqurq6Ii4szia0oCtatW4cBAwbgjTfeQIcOHXD8+HFcvXoVoaGhcHBwQI8ePZCTk2P8n6ioKAwaNMhkOjExMQgNDTV+Dg0NxWeffYZZs2ahadOmaNWqFRYuXFghdnn3eE9PTwBA586doSgKQkNDkZGRgQYNGqCwsLBCrPfee69mK5uIiMiCMGknIiKycHfv3kVKSgqmTJkCBweHCuXOzs4wGAyIiIjA3bt3ceTIERw6dAi5ubn45JNPTH6bk5OD5ORkpKSkICEhARs3bsRHH32EGzdu4MiRI1i6dCnmzZuHkydPmvxfbGwsIiMjce7cOfj6+mLEiBGYOHEi5s6dizNnzkBEMHXq1Ndeth9//BEODg44efIkli1bhm+//RaHDh2q9LenTp0CAPz+++8oKChAYmIigoOD4eXlha1btxp/V1paim3btmHMmDGvPT9ERESWht3jiYiILNzVq1chIvD19a3yN6mpqbh48SKuXbsGNzc3AMCWLVvQsWNHnD59Gl27dgUAGAwGbNq0CU5OTvDz80Pv3r1x5coV/Pbbb9DpdGjfvj2WLl2Kw4cPIzAw0Dj90aNHY+jQoQCA2bNnIygoCPPnz0dYWBgAYPr06Rg9evRrL1unTp2wYMECAICPjw9Wr16N1NRU9OvXr8JvW7RoAQBo1qyZSbf5sWPHIj4+HjNnzgQA7Nu3DyUlJcb5JSIiqs94p52IiMjCichLf5OVlQU3Nzdjwg4Afn5+cHZ2RlZWlvE7Dw8PODk5GT+3bNkSfn5+0Ol0Jt/duXPHZPqdOnUyKQeAgIAAk+9KSkrw4MGD11gy0+kCgF6vrxD7ZaKionD16lXjYwKbN2/G0KFDK+2VQEREVN/wTjsREZGF8/HxgaIoqgw216BBA5PPiqJU+p3BYKjy/xRFqfK78v/T6XQVGhtKS0tfaX7+G/tlXF1dER4ejvj4eHh6eiI5ORnp6emvNQ0iIiJLxTvtREREFq5p06YICwvDmjVr8OjRowrl9+/fR4cOHZCfn4/8/Hzj95cuXcL9+/fh5+en5ewCeN6VvaCgwOQ7c1/VZmdnBwAoKyurUDZu3Djs3LkT69evh7e3N3r27GlWLCIiIkvBpJ2IiKgeWLNmDcrKytCtWzfs3r0b2dnZyMrKwsqVKxEUFIS+ffsiICAAn376Kc6ePYtTp04hMjISISEhePfddzWf3z59+uDMmTPYsmULsrOzsWDBAvzxxx9mTdPV1RWNGjVCSkoKbt++jaKiImNZWFgYGjdujEWLFtXo2XoiIiJLxaSdiIioHvDy8sLZs2fRu3dvfPHFF/D390e/fv2QmpqKtWvXQlEU7N27Fy4uLggODkbfvn3h5eWFnTt31sn8hoWFYf78+Zg1axa6du2K4uJiREZGmjVNW1tbrFy5EuvWrUPr1q0RERFhLNPpdIiKikJZWZnZcYiIiCyJIq8yug0RERGRhRs7diz+/vtvJCUl1fWsEBERqYYD0REREVG9VlRUhIsXL2L79u1M2ImIyOowaSciIqJ6LSIiAqdOncKkSZMqfb87ERFRfcbu8UREREREREQWigPREREREREREVkoJu1EREREREREFopJOxEREREREZGFYtJOREREREREZKGYtBMRERERERFZKCbtRERERERERBaKSTsRERERERGRhWLSTkRERERERGShmLQTERERERERWaj/A6VCfANGmLM9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "communities.value_counts().sort_values(ascending=False).plot(kind=\"bar\", figsize=(12, 5))\n",
+    "plt.xlabel(\"Community\")\n",
+    "plt.ylabel(\"# Members\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:uae,pretax profit,gcc,profitable,iranian,bahrain,merchant,portugal,saudi arabia,arabian\n",
+      "1:spokeswoman,rand,television,billion marks,schlesinger,g-7,majority,anti,und,reporters\n",
+      "2:compensation,plan,previously announced,real estate,jwt,pan,colonial,paid,undisclosed,democrat\n",
+      "3:wholly,realty,excluding,sanctions,commercial,times,williams,reflected,depressed,administration\n",
+      "4:brazilian,newspaper,better,intervened,subroto,talks,drug,ceiling,actively,arango\n",
+      "5:yugoslavia,equipment,dome,allis,nova,sec,petroleum,america,pak,mississippi\n",
+      "6:oklahoma,railroad,magazine,shipbuilding,soybean,area,plains,previous,temperatures,dry\n",
+      "7:drill,small,group net,mint,acres,acre,net income,eagle,glass,aug\n",
+      "8:assistance,acquire,carl,emery,western,application,carson,orange,the bank of england,fcoj\n",
+      "9:moscow,maximum,india,agriculture,indian,ministers,senate,mln tonnes,usda,offering\n",
+      "10:dispute,wagner,recapitalization,harcourt,reynolds,reed,jersey,salomon,bancroft,broadcast\n",
+      "11:value,miguel,jordan,guarantee,scheduled,program initiative announced,kong,tonne,common,issue\n",
+      "12:social,followed,net profit,utility,mln francs,billion francs,statistics,consumer,planning,shipment trade sources\n",
+      "13:includes,billion,corp,sets,shrs,oper shr,quarterly,oper,note,prior qtr\n",
+      "14:denshin,independent,australia,australian,expressed,maker,shoe,periods ended,fairchild,work\n",
+      "15:voting,institutions,mark,lira,affiliate,donald,davis,trump,greek,premium\n",
+      "16:earn,internal,old,vista,near,financing,substantial,motors,centers,beneficial\n",
+      "17:sulphur,light,bbl,remained,distillate,edmonton,potential,demand,citgo,family\n",
+      "18:loans,coconut,agreements,dividend payable,bank,grew,balance,n.y.,short,mutual\n",
+      "19:rotterdam,margins,liquidity shortage,waiting,bills,forecast,shortage,late,circulation,gem\n",
+      "20:ohio,insurance benefits,actually,jobless,wash,commonwealth,columbia,state programs,edison,claims\n",
+      "21:takes,alusuisse,results reflect,royalty,warrants,illinois,steam,ford,belgian,enterprise\n",
+      "22:textile,need,advisor,stage,options,king,retaliatory,year ended,club,construction\n",
+      "23:sell,unit\n",
+      "24:release,preliminary,calif.,animal,conversion,bayou,benefit,calif,immediate,approximately\n",
+      "25:ameritrust,shortly,norstar,dart,district,warner,hudson,begins,gaf,citicorp\n",
+      "26:spokesman told,taiwan,reuters,told,versus,guesstimated,house,laws,chicago,week ago\n",
+      "27:hospital,legislation,uruguay,congress,heller,appropriate,arco,barge,bilateral,clayton\n",
+      "28:definitive agreement,combined\n",
+      "29:pct stake,businesses,grades,singapore,strait,quarter ending,petrol,ecuador,bangladesh,provided\n",
+      "30:indonesian,francs,conference,rubber,largest,adjustment,economist,needs,xuto,pending\n",
+      "31:according,increase,force,workers,farmers,ports,representing,meet,job,living\n",
+      "32:higher,quarter,earned,ended,sees\n",
+      "33:profits,years\n",
+      "34:parent,turnover\n",
+      "35:primary,diluted,shr primary\n"
+     ]
+    }
+   ],
+   "source": [
+    "for comm_index in set(communities.values):\n",
+    "    nodes = communities[communities==comm_index].index\n",
+    "    print(f\"{comm_index}:\" + \",\".join(nodes[:10]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comm_index = communities.loc[\"turkish\"]\n",
+    "nodes = communities[communities==comm_index].index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "smallGrap = nx.subgraph(filteredEntityGraph, nbunch=nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,7))\n",
+    "\n",
+    "pos = nx.spring_layout(smallGrap) # k regulates the distance between nodes\n",
+    "\n",
+    "nx.draw(smallGrap, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos)\n",
+    "\n",
+    "# plt.show()\n",
+    "# plt.savefig(os.path.join(\".\", \"CloseUp.png\"), dpi=300, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we show a Bipartite Closeup of the cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bipartiteCloseup = subGraph.edge_subgraph(\n",
+    "    {e for e in subGraph.edges() if len(set(e).intersection(nodes))>0}\n",
+    ")\n",
+    "\n",
+    "deg = nx.degree(bipartiteCloseup)\n",
+    "\n",
+    "smallGrap = nx.subgraph(bipartiteCloseup, {n for n, d in bipartiteCloseup.nodes(data=True) if d[\"bipartite\"]==1 or deg[n]>1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "299"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len([n for n, d in bipartiteCloseup.nodes(data=True) if d[\"bipartite\"]==0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,7))\n",
+    "\n",
+    "pos = nx.kamada_kawai_layout(smallGrap) # k regulates the distance between nodes\n",
+    "\n",
+    "node_color = [\"skyblue\" if d[\"bipartite\"]==1 else \"red\" for n, d in smallGrap.nodes(data=True)]\n",
+    "\n",
+    "nx.draw(smallGrap, with_labels=False, node_color=node_color, #'skyblue', \n",
+    "        node_size=150, edge_cmap=plt.cm.Blues, pos = pos)\n",
+    "\n",
+    "\n",
+    "# plt.show()\n",
+    "# plt.savefig(os.path.join(\".\", \"BipartiteCloseUp.png\"), dpi=300, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analysis of the relation between Turkey and Greece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "      <th>triplets</th>\n",
+       "      <th>keywords</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>training/10539</th>\n",
+       "      <td>NATO CALLS ON GREECE AND TURKEY TO AVOID FORCE...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(NATO, CALLS, ON, GREECE, AND, TURKEY, TO, AVO...</td>\n",
+       "      <td>[(CALLS, (AVOID, False), Greece), (CALLS, (AVO...</td>\n",
+       "      <td>[(situation, 0.2472109039575448), (carrington,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/10395</th>\n",
+       "      <td>PAPANDREOU SAYS GREEKS READY FOR AGGRESSORS  G...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(PAPANDREOU, SAYS, GREEKS, READY, FOR, AGGRESS...</td>\n",
+       "      <td>[(PAPANDREOU, (SAYS, False), READY), (Papandre...</td>\n",
+       "      <td>[(greek, 0.3887438310146141), (papandreou, 0.2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/10621</th>\n",
+       "      <td>PAPANDREOU SHOWS \"RESTRICTED OPTIMISM\" OVER CR...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(PAPANDREOU, SHOWS, \", RESTRICTED, OPTIMISM, \"...</td>\n",
+       "      <td>[(Papandreou, (expressed, False), optimism), (...</td>\n",
+       "      <td>[(greek, 0.38800525497567984), (leader, 0.2340...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/10797</th>\n",
+       "      <td>GREECE SCRAPS U.S. BASE CLOSURE REQUEST  Prime...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(GREECE, SCRAPS, U.S., BASE, CLOSURE, REQUEST,...</td>\n",
+       "      <td>[(Papandreou, (withdrawn, False), request), (r...</td>\n",
+       "      <td>[(papandreou, 0.25551082676438086), (aegean, 0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/10627</th>\n",
+       "      <td>TURKISH-GREEK AEGEAN TENSION ABATES  Turkey\"s ...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(TURKISH, -, GREEK, AEGEAN, TENSION, ABATES,  ...</td>\n",
+       "      <td>[(TENSION, (ABATES, False), standoff), (Turkey...</td>\n",
+       "      <td>[(aegean, 0.2606324396779156), (said, 0.245027...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/10641</th>\n",
+       "      <td>TURKEY LIFTS SURVEY SHIP ESCORT AS TENSION ABA...</td>\n",
+       "      <td>[crude, ship]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(TURKEY, LIFTS, SURVEY, SHIP, ESCORT, AS, TENS...</td>\n",
+       "      <td>[(ESCORT, (pulled, False), warships), (Turkey,...</td>\n",
+       "      <td>[(turkish waters, 0.2064748955978376), (rights...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/15200</th>\n",
+       "      <td>TURKEY CALLS FOR DIALOGUE TO SOLVE DISPUTE  Tu...</td>\n",
+       "      <td>[crude]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(TURKEY, CALLS, FOR, DIALOGUE, TO, SOLVE, DISP...</td>\n",
+       "      <td>[(agreement, (effect, False), security), (coun...</td>\n",
+       "      <td>[(said, 0.2734395997182076), (turkey, 0.231337...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/835</th>\n",
+       "      <td>GREECE SAYS IT HAS RIGHT ON AEGEAN OIL DRILLIN...</td>\n",
+       "      <td>[crude]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(GREECE, SAYS, IT, HAS, RIGHT, ON, AEGEAN, OIL...</td>\n",
+       "      <td>[(warning, (conducting, False), activities), (...</td>\n",
+       "      <td>[(greek, 0.32157298143397534), (aegean, 0.2623...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       clean_text  \\\n",
+       "id                                                                  \n",
+       "training/10539  NATO CALLS ON GREECE AND TURKEY TO AVOID FORCE...   \n",
+       "training/10395  PAPANDREOU SAYS GREEKS READY FOR AGGRESSORS  G...   \n",
+       "training/10621  PAPANDREOU SHOWS \"RESTRICTED OPTIMISM\" OVER CR...   \n",
+       "training/10797  GREECE SCRAPS U.S. BASE CLOSURE REQUEST  Prime...   \n",
+       "training/10627  TURKISH-GREEK AEGEAN TENSION ABATES  Turkey\"s ...   \n",
+       "training/10641  TURKEY LIFTS SURVEY SHIP ESCORT AS TENSION ABA...   \n",
+       "test/15200      TURKEY CALLS FOR DIALOGUE TO SOLVE DISPUTE  Tu...   \n",
+       "training/835    GREECE SAYS IT HAS RIGHT ON AEGEAN OIL DRILLIN...   \n",
+       "\n",
+       "                        label language  \\\n",
+       "id                                       \n",
+       "training/10539  [crude, ship]       en   \n",
+       "training/10395  [crude, ship]       en   \n",
+       "training/10621  [crude, ship]       en   \n",
+       "training/10797  [crude, ship]       en   \n",
+       "training/10627  [crude, ship]       en   \n",
+       "training/10641  [crude, ship]       en   \n",
+       "test/15200            [crude]       en   \n",
+       "training/835          [crude]       en   \n",
+       "\n",
+       "                                                           parsed  \\\n",
+       "id                                                                  \n",
+       "training/10539  (NATO, CALLS, ON, GREECE, AND, TURKEY, TO, AVO...   \n",
+       "training/10395  (PAPANDREOU, SAYS, GREEKS, READY, FOR, AGGRESS...   \n",
+       "training/10621  (PAPANDREOU, SHOWS, \", RESTRICTED, OPTIMISM, \"...   \n",
+       "training/10797  (GREECE, SCRAPS, U.S., BASE, CLOSURE, REQUEST,...   \n",
+       "training/10627  (TURKISH, -, GREEK, AEGEAN, TENSION, ABATES,  ...   \n",
+       "training/10641  (TURKEY, LIFTS, SURVEY, SHIP, ESCORT, AS, TENS...   \n",
+       "test/15200      (TURKEY, CALLS, FOR, DIALOGUE, TO, SOLVE, DISP...   \n",
+       "training/835    (GREECE, SAYS, IT, HAS, RIGHT, ON, AEGEAN, OIL...   \n",
+       "\n",
+       "                                                         triplets  \\\n",
+       "id                                                                  \n",
+       "training/10539  [(CALLS, (AVOID, False), Greece), (CALLS, (AVO...   \n",
+       "training/10395  [(PAPANDREOU, (SAYS, False), READY), (Papandre...   \n",
+       "training/10621  [(Papandreou, (expressed, False), optimism), (...   \n",
+       "training/10797  [(Papandreou, (withdrawn, False), request), (r...   \n",
+       "training/10627  [(TENSION, (ABATES, False), standoff), (Turkey...   \n",
+       "training/10641  [(ESCORT, (pulled, False), warships), (Turkey,...   \n",
+       "test/15200      [(agreement, (effect, False), security), (coun...   \n",
+       "training/835    [(warning, (conducting, False), activities), (...   \n",
+       "\n",
+       "                                                         keywords  \n",
+       "id                                                                 \n",
+       "training/10539  [(situation, 0.2472109039575448), (carrington,...  \n",
+       "training/10395  [(greek, 0.3887438310146141), (papandreou, 0.2...  \n",
+       "training/10621  [(greek, 0.38800525497567984), (leader, 0.2340...  \n",
+       "training/10797  [(papandreou, 0.25551082676438086), (aegean, 0...  \n",
+       "training/10627  [(aegean, 0.2606324396779156), (said, 0.245027...  \n",
+       "training/10641  [(turkish waters, 0.2064748955978376), (rights...  \n",
+       "test/15200      [(said, 0.2734395997182076), (turkey, 0.231337...  \n",
+       "training/835    [(greek, 0.32157298143397534), (aegean, 0.2623...  "
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_ids_turkey=list(nx.neighbors(smallGrap, \"turkey\"))\n",
+    "doc_ids_greece=list(nx.neighbors(smallGrap, \"greece\"))\n",
+    "\n",
+    "doc_ids=set(doc_ids_turkey).intersection(doc_ids_greece)\n",
+    "\n",
+    "corpus.loc[list(doc_ids)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using TSNE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|██████████████████████████| 2267/2267 [00:02<00:00, 1029.14it/s]\n",
+      "Generating walks (CPU: 1): 100%|███████████████████████████████████████| 500/500 [40:26<00:00,  4.85s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from node2vec import Node2Vec\n",
+    "\n",
+    "node2vec = Node2Vec(filteredEntityGraph, dimensions=5, num_walks=200, workers=4, quiet=True) \n",
+    "model = node2vec.fit(window=10) \n",
+    "embeddings = model.wv "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "tsne=TSNE(n_components=2)\n",
+    "embedding2d=tsne.fit_transform(embeddings.vectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x709dd5cf6af0>]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(embedding2d[:, 0], embedding2d[:, 1], 'o')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using Node2Vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Node2Vec allows also to compute a similarity between entities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('adjustments', 0.9959986805915833),\n",
+       " ('governments', 0.9918233752250671),\n",
+       " ('greece', 0.9860679507255554),\n",
+       " ('agreed', 0.9789270162582397),\n",
+       " ('greek', 0.9773082733154297),\n",
+       " ('franc', 0.9757379293441772),\n",
+       " ('inch', 0.9734088182449341),\n",
+       " ('problems', 0.9733425974845886),\n",
+       " ('damage', 0.9668214917182922),\n",
+       " ('athens', 0.9657086730003357)]"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embeddings.most_similar(positive=[\"turkey\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Document-Document Graph Projection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from networkx.algorithms.bipartite.projection import overlap_weighted_projected_graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documentGraph = overlap_weighted_projected_graph(\n",
+    "    G, \n",
+    "    {n for n, d in G.nodes(data=True) if d[\"bipartite\"] == 0}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 10788\n",
+      "Number of edges: 13061229\n",
+      "Average degree: 2421.4366\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(documentGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degrees = pd.Series({k: v for k, v in nx.degree(documentGraph)}, name=\"degree\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in documentGraph.edges(data=True)}, name=\"edge_weight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plotDistribution(degrees, 13, minValue=1E0)\n",
+    "plt.yscale(\"log\")\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plotDistribution(allEdgesWeights, 20)\n",
+    "plt.xlim([1E-2, 10])\n",
+    "plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filteredDocumentGraph = documentGraph.edge_subgraph(\n",
+    "    allEdgesWeights[(allEdgesWeights>0.6)].index.tolist()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 1942\n",
+      "Number of edges: 7961\n",
+      "Average degree:   8.1988\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(filteredDocumentGraph))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Global and Local Properties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degrees = pd.Series({k: v for k, v in nx.degree(filteredDocumentGraph)}, name=\"degree\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in filteredDocumentGraph.edges(data=True)}, name=\"edge_weight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 2)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plotDistribution(degrees, 13, minValue=1E0)\n",
+    "plt.yscale(\"log\")\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plotDistribution(allEdgesWeights, 20)\n",
+    "plt.xlim([1E-2, 10])\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlim([0.1, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Core - Periphery Description and Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create network layout for visualizations\n",
+    "spring_pos = nx.spring_layout(filteredDocumentGraph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_edge_color = 'gray'\n",
+    "default_node_color = '#407cc9'\n",
+    "enhanced_node_color = '#f5b042'\n",
+    "enhanced_edge_color = '#cc2f04'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.axis(\"off\")\n",
+    "nx.draw_networkx(filteredDocumentGraph, pos=spring_pos, node_color=default_node_color, \n",
+    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "components = pd.Series({ith: component \n",
+    "              for ith, component in enumerate(nx.connected_components(filteredDocumentGraph))})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "components_size = components.apply(len)\n",
+    "components_size.name = \"size\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDistribution(components_size, nbins=20)\n",
+    "plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coreDocumentGraph = nx.subgraph(\n",
+    "    filteredDocumentGraph,\n",
+    "    [node for nodes in components[components.apply(len)>8].values for node in nodes]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# To be visualized in Gephi\n",
+    "nx.write_gexf(coreDocumentGraph,\"coreGraph.gexf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 1053\n",
+      "Number of edges: 7198\n",
+      "Average degree:  13.6714\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(coreDocumentGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "degrees = pd.Series({k: v for k, v in nx.degree(coreDocumentGraph)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDistribution(degrees, 100, minValue=1E0)\n",
+    "plt.yscale(\"log\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in coreDocumentGraph.edges(data=True)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/matplotlib/axes/_axes.py:6703: RuntimeWarning: invalid value encountered in multiply\n",
+      "  boffset = -0.5 * dr * totwidth * (1 - 1 / nx)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 1)"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDistribution(allEdgesWeights, 100)\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlim([1E-1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create network layout for visualizations\n",
+    "spring_pos = nx.spring_layout(coreDocumentGraph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_edge_color = 'gray'\n",
+    "default_node_color = '#407cc9'\n",
+    "enhanced_node_color = '#f5b042'\n",
+    "enhanced_edge_color = '#cc2f04'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.axis(\"off\")\n",
+    "nx.draw_networkx(coreDocumentGraph, pos=spring_pos, node_color=default_node_color, \n",
+    "                 edge_color=default_edge_color, with_labels=False, node_size=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Community Detection and Topics Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "communities = pd.Series(community.best_partition(filteredDocumentGraph))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4      194\n",
+       "14     167\n",
+       "13     155\n",
+       "2      123\n",
+       "16     100\n",
+       "      ... \n",
+       "348      2\n",
+       "350      2\n",
+       "373      2\n",
+       "374      2\n",
+       "262      2\n",
+       "Name: count, Length: 390, dtype: int64"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities.value_counts().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "def getTopicRatio(df):\n",
+    "    return Counter([label for labels in df[\"label\"] for label in labels])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "communityTopics = pd.DataFrame.from_dict({\n",
+    "    cid: getTopicRatio(corpus.loc[comm.index])\n",
+    "    for cid, comm in communities.groupby(communities)\n",
+    "}, orient=\"index\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalizedCommunityTopics = (communityTopics.T / communityTopics.sum(axis=1)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x709b5d9fe520>"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "normalizedCommunityTopics.std(axis=1).fillna(0).hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(390, 58)"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalizedCommunityTopics.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Frequency')"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "normalizedCommunityTopics.apply(lambda x: np.mean(-np.log(x)), axis=1).hist()\n",
+    "plt.xlabel(\"Entropy\")\n",
+    "plt.ylabel(\"Frequency\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topicsCorrelation = normalizedCommunityTopics.corr().fillna(0)\n",
+    "topicsCorrelation[topicsCorrelation<0.8]=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topicsGraph = nx.from_pandas_adjacency(topicsCorrelation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,6))\n",
+    "\n",
+    "pos = nx.spring_layout(topicsGraph, k=0.3) # k regulates the distance between nodes\n",
+    "\n",
+    "nx.draw(topicsGraph, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos)\n",
+    "\n",
+    "# plt.show()\n",
+    "# plt.savefig(os.path.join(\".\", \"TopicsAll.png\"), dpi=300, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filteredTopicsGraph = nx.subgraph(\n",
+    "    topicsGraph,\n",
+    "    [node for component in nx.connected_components(topicsGraph) if len(component)>3 for node in component]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,6))\n",
+    "\n",
+    "pos = nx.kamada_kawai_layout(filteredTopicsGraph) # k regulates the distance between nodes\n",
+    "\n",
+    "nx.draw(filteredTopicsGraph, with_labels=True, node_color='skyblue', node_size=1500, \n",
+    "        edge_cmap=plt.cm.Blues, pos = pos)\n",
+    "\n",
+    "# plt.show()\n",
+    "# plt.savefig(os.path.join(\".\", \"TopicsCore.png\"), dpi=300, format=\"png\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# To be visualized in Gephi\n",
+    "nx.write_gexf(coreDocumentGraph, \"coreDocumentGraph\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Embeddings for the Document-Document Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|████████████████████████████| 1053/1053 [00:24<00:00, 43.65it/s]\n",
+      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████| 10/10 [00:20<00:00,  2.09s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from node2vec import Node2Vec\n",
+    "\n",
+    "node2vec = Node2Vec(coreDocumentGraph, dimensions=20) \n",
+    "model = node2vec.fit(window=10) \n",
+    "embeddings = model.wv "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "tsne=TSNE(n_components=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding2d=tsne.fit_transform(embeddings.vectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x709dd517a340>]"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(embedding2d[:, 0], embedding2d[:, 1], 'o')"
+   ]
+  },
+  {
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+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
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+       "      <td>-6.560338</td>\n",
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+       "      <td>5.412939</td>\n",
+       "      <td>-3.564779</td>\n",
+       "      <td>-0.057957</td>\n",
+       "      <td>-0.688464</td>\n",
+       "      <td>2.905470</td>\n",
+       "      <td>1.557411</td>\n",
+       "      <td>-3.210068</td>\n",
+       "      <td>0.827968</td>\n",
+       "      <td>-0.532490</td>\n",
+       "      <td>0.003652</td>\n",
+       "      <td>-0.121509</td>\n",
+       "      <td>-1.665959</td>\n",
+       "      <td>-0.950425</td>\n",
+       "      <td>-0.382220</td>\n",
+       "      <td>4.117815</td>\n",
+       "      <td>-1.906440</td>\n",
+       "      <td>-3.899600</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-6.391430</td>\n",
+       "      <td>1.779750</td>\n",
+       "      <td>0.462306</td>\n",
+       "      <td>2.715431</td>\n",
+       "      <td>-2.103771</td>\n",
+       "      <td>-1.989185</td>\n",
+       "      <td>-1.984643</td>\n",
+       "      <td>0.105770</td>\n",
+       "      <td>4.286595</td>\n",
+       "      <td>-3.886077</td>\n",
+       "      <td>-0.450857</td>\n",
+       "      <td>0.282356</td>\n",
+       "      <td>-1.321213</td>\n",
+       "      <td>3.158543</td>\n",
+       "      <td>0.123383</td>\n",
+       "      <td>-0.627238</td>\n",
+       "      <td>1.957629</td>\n",
+       "      <td>3.836007</td>\n",
+       "      <td>-2.467475</td>\n",
+       "      <td>-0.543244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-5.361574</td>\n",
+       "      <td>0.622695</td>\n",
+       "      <td>0.208257</td>\n",
+       "      <td>-3.572732</td>\n",
+       "      <td>0.091671</td>\n",
+       "      <td>-1.721061</td>\n",
+       "      <td>1.046673</td>\n",
+       "      <td>2.048406</td>\n",
+       "      <td>4.613693</td>\n",
+       "      <td>-1.404773</td>\n",
+       "      <td>2.801529</td>\n",
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+       "      <td>0.129688</td>\n",
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+       "      <td>0.148159</td>\n",
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+       "      <td>-0.830270</td>\n",
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+       "      <td>-0.654220</td>\n",
+       "      <td>-0.372447</td>\n",
+       "      <td>0.006526</td>\n",
+       "      <td>-0.608662</td>\n",
+       "      <td>0.442872</td>\n",
+       "      <td>-0.374222</td>\n",
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+       "    <tr>\n",
+       "      <th>1049</th>\n",
+       "      <td>0.207729</td>\n",
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+       "      <td>-0.062639</td>\n",
+       "      <td>-0.489831</td>\n",
+       "      <td>0.296728</td>\n",
+       "      <td>0.145687</td>\n",
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+       "      <td>-0.592900</td>\n",
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+       "      <td>0.490716</td>\n",
+       "      <td>1.043012</td>\n",
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+       "      <th>1050</th>\n",
+       "      <td>0.989873</td>\n",
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+       "      <td>0.298057</td>\n",
+       "      <td>0.014688</td>\n",
+       "      <td>0.094849</td>\n",
+       "      <td>0.229320</td>\n",
+       "      <td>0.049907</td>\n",
+       "      <td>0.450760</td>\n",
+       "      <td>-0.672756</td>\n",
+       "      <td>-0.095832</td>\n",
+       "      <td>0.664670</td>\n",
+       "      <td>0.030931</td>\n",
+       "      <td>0.212077</td>\n",
+       "      <td>0.508668</td>\n",
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+       "      <td>-0.273089</td>\n",
+       "      <td>-0.043678</td>\n",
+       "      <td>-0.976209</td>\n",
+       "      <td>0.072014</td>\n",
+       "      <td>0.067489</td>\n",
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+       "    <tr>\n",
+       "      <th>1051</th>\n",
+       "      <td>0.597600</td>\n",
+       "      <td>-0.274929</td>\n",
+       "      <td>-0.519920</td>\n",
+       "      <td>0.653873</td>\n",
+       "      <td>0.242664</td>\n",
+       "      <td>0.253243</td>\n",
+       "      <td>-0.371124</td>\n",
+       "      <td>0.603747</td>\n",
+       "      <td>-0.012600</td>\n",
+       "      <td>0.302335</td>\n",
+       "      <td>-0.660933</td>\n",
+       "      <td>-0.391477</td>\n",
+       "      <td>0.386280</td>\n",
+       "      <td>0.251975</td>\n",
+       "      <td>0.099648</td>\n",
+       "      <td>0.507324</td>\n",
+       "      <td>0.229800</td>\n",
+       "      <td>-1.581192</td>\n",
+       "      <td>-0.340255</td>\n",
+       "      <td>-0.109365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1052</th>\n",
+       "      <td>0.296712</td>\n",
+       "      <td>0.237102</td>\n",
+       "      <td>0.165965</td>\n",
+       "      <td>0.044456</td>\n",
+       "      <td>0.055577</td>\n",
+       "      <td>-0.053818</td>\n",
+       "      <td>0.435192</td>\n",
+       "      <td>0.170955</td>\n",
+       "      <td>-0.636491</td>\n",
+       "      <td>0.638219</td>\n",
+       "      <td>0.501777</td>\n",
+       "      <td>-0.471974</td>\n",
+       "      <td>-0.257314</td>\n",
+       "      <td>0.102539</td>\n",
+       "      <td>-0.315415</td>\n",
+       "      <td>0.122501</td>\n",
+       "      <td>0.329387</td>\n",
+       "      <td>-0.484986</td>\n",
+       "      <td>0.351573</td>\n",
+       "      <td>0.432368</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1053 rows × 20 columns</p>\n",
+       "</div>"
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+       "4    -5.361574  0.622695  0.208257 -3.572732  0.091671 -1.721061  1.046673   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
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+       "\n",
+       "            7         8         9         10        11        12        13  \\\n",
+       "0     2.903708  1.336813 -3.476732  1.427167 -0.266681 -0.471343  0.184137   \n",
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+       "1052  0.170955 -0.636491  0.638219  0.501777 -0.471974 -0.257314  0.102539   \n",
+       "\n",
+       "            14        15        16        17        18        19  \n",
+       "0    -2.069631 -0.574260 -0.738087  3.788915 -1.683529 -4.035486  \n",
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+       "3     0.123383 -0.627238  1.957629  3.836007 -2.467475 -0.543244  \n",
+       "4    -2.543316 -0.761218  2.574573  5.359656 -1.035850 -3.710101  \n",
+       "...        ...       ...       ...       ...       ...       ...  \n",
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+       "1052 -0.315415  0.122501  0.329387 -0.484986  0.351573  0.432368  \n",
+       "\n",
+       "[1053 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(embeddings.vectors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
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+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/20368</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/20221</th>\n",
+       "      <td>-6.560338</td>\n",
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+       "      <td>0.892895</td>\n",
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+       "      <td>-0.158013</td>\n",
+       "      <td>0.018590</td>\n",
+       "      <td>-1.647612</td>\n",
+       "      <td>-0.788695</td>\n",
+       "      <td>-0.325296</td>\n",
+       "      <td>3.511575</td>\n",
+       "      <td>-1.597107</td>\n",
+       "      <td>-3.987545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/20442</th>\n",
+       "      <td>-6.648391</td>\n",
+       "      <td>3.095893</td>\n",
+       "      <td>0.785576</td>\n",
+       "      <td>5.412939</td>\n",
+       "      <td>-3.564779</td>\n",
+       "      <td>-0.057957</td>\n",
+       "      <td>-0.688464</td>\n",
+       "      <td>2.905470</td>\n",
+       "      <td>1.557411</td>\n",
+       "      <td>-3.210068</td>\n",
+       "      <td>0.827968</td>\n",
+       "      <td>-0.532490</td>\n",
+       "      <td>0.003652</td>\n",
+       "      <td>-0.121509</td>\n",
+       "      <td>-1.665959</td>\n",
+       "      <td>-0.950425</td>\n",
+       "      <td>-0.382220</td>\n",
+       "      <td>4.117815</td>\n",
+       "      <td>-1.906440</td>\n",
+       "      <td>-3.899600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/20800</th>\n",
+       "      <td>-6.391430</td>\n",
+       "      <td>1.779750</td>\n",
+       "      <td>0.462306</td>\n",
+       "      <td>2.715431</td>\n",
+       "      <td>-2.103771</td>\n",
+       "      <td>-1.989185</td>\n",
+       "      <td>-1.984643</td>\n",
+       "      <td>0.105770</td>\n",
+       "      <td>4.286595</td>\n",
+       "      <td>-3.886077</td>\n",
+       "      <td>-0.450857</td>\n",
+       "      <td>0.282356</td>\n",
+       "      <td>-1.321213</td>\n",
+       "      <td>3.158543</td>\n",
+       "      <td>0.123383</td>\n",
+       "      <td>-0.627238</td>\n",
+       "      <td>1.957629</td>\n",
+       "      <td>3.836007</td>\n",
+       "      <td>-2.467475</td>\n",
+       "      <td>-0.543244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/3971</th>\n",
+       "      <td>-5.361574</td>\n",
+       "      <td>0.622695</td>\n",
+       "      <td>0.208257</td>\n",
+       "      <td>-3.572732</td>\n",
+       "      <td>0.091671</td>\n",
+       "      <td>-1.721061</td>\n",
+       "      <td>1.046673</td>\n",
+       "      <td>2.048406</td>\n",
+       "      <td>4.613693</td>\n",
+       "      <td>-1.404773</td>\n",
+       "      <td>2.801529</td>\n",
+       "      <td>2.242345</td>\n",
+       "      <td>1.547014</td>\n",
+       "      <td>6.425769</td>\n",
+       "      <td>-2.543316</td>\n",
+       "      <td>-0.761218</td>\n",
+       "      <td>2.574573</td>\n",
+       "      <td>5.359656</td>\n",
+       "      <td>-1.035850</td>\n",
+       "      <td>-3.710101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/1134</th>\n",
+       "      <td>0.129688</td>\n",
+       "      <td>0.868474</td>\n",
+       "      <td>0.158408</td>\n",
+       "      <td>0.148159</td>\n",
+       "      <td>0.302053</td>\n",
+       "      <td>0.617617</td>\n",
+       "      <td>-1.176051</td>\n",
+       "      <td>-0.358285</td>\n",
+       "      <td>-0.874892</td>\n",
+       "      <td>-0.830270</td>\n",
+       "      <td>-1.113515</td>\n",
+       "      <td>-0.046454</td>\n",
+       "      <td>0.627627</td>\n",
+       "      <td>-1.529335</td>\n",
+       "      <td>-0.654220</td>\n",
+       "      <td>-0.372447</td>\n",
+       "      <td>0.006526</td>\n",
+       "      <td>-0.608662</td>\n",
+       "      <td>0.442872</td>\n",
+       "      <td>-0.374222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/4956</th>\n",
+       "      <td>0.207729</td>\n",
+       "      <td>-0.256361</td>\n",
+       "      <td>-0.847864</td>\n",
+       "      <td>0.055823</td>\n",
+       "      <td>-0.062639</td>\n",
+       "      <td>-0.489831</td>\n",
+       "      <td>0.296728</td>\n",
+       "      <td>0.145687</td>\n",
+       "      <td>-1.076312</td>\n",
+       "      <td>-0.194407</td>\n",
+       "      <td>-0.076653</td>\n",
+       "      <td>0.206450</td>\n",
+       "      <td>-0.603945</td>\n",
+       "      <td>0.863203</td>\n",
+       "      <td>0.882457</td>\n",
+       "      <td>0.478035</td>\n",
+       "      <td>-0.592900</td>\n",
+       "      <td>-1.803185</td>\n",
+       "      <td>0.490716</td>\n",
+       "      <td>1.043012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/11154</th>\n",
+       "      <td>0.989873</td>\n",
+       "      <td>0.660073</td>\n",
+       "      <td>0.298057</td>\n",
+       "      <td>0.014688</td>\n",
+       "      <td>0.094849</td>\n",
+       "      <td>0.229320</td>\n",
+       "      <td>0.049907</td>\n",
+       "      <td>0.450760</td>\n",
+       "      <td>-0.672756</td>\n",
+       "      <td>-0.095832</td>\n",
+       "      <td>0.664670</td>\n",
+       "      <td>0.030931</td>\n",
+       "      <td>0.212077</td>\n",
+       "      <td>0.508668</td>\n",
+       "      <td>-0.015358</td>\n",
+       "      <td>-0.273089</td>\n",
+       "      <td>-0.043678</td>\n",
+       "      <td>-0.976209</td>\n",
+       "      <td>0.072014</td>\n",
+       "      <td>0.067489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/5914</th>\n",
+       "      <td>0.597600</td>\n",
+       "      <td>-0.274929</td>\n",
+       "      <td>-0.519920</td>\n",
+       "      <td>0.653873</td>\n",
+       "      <td>0.242664</td>\n",
+       "      <td>0.253243</td>\n",
+       "      <td>-0.371124</td>\n",
+       "      <td>0.603747</td>\n",
+       "      <td>-0.012600</td>\n",
+       "      <td>0.302335</td>\n",
+       "      <td>-0.660933</td>\n",
+       "      <td>-0.391477</td>\n",
+       "      <td>0.386280</td>\n",
+       "      <td>0.251975</td>\n",
+       "      <td>0.099648</td>\n",
+       "      <td>0.507324</td>\n",
+       "      <td>0.229800</td>\n",
+       "      <td>-1.581192</td>\n",
+       "      <td>-0.340255</td>\n",
+       "      <td>-0.109365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/554</th>\n",
+       "      <td>0.296712</td>\n",
+       "      <td>0.237102</td>\n",
+       "      <td>0.165965</td>\n",
+       "      <td>0.044456</td>\n",
+       "      <td>0.055577</td>\n",
+       "      <td>-0.053818</td>\n",
+       "      <td>0.435192</td>\n",
+       "      <td>0.170955</td>\n",
+       "      <td>-0.636491</td>\n",
+       "      <td>0.638219</td>\n",
+       "      <td>0.501777</td>\n",
+       "      <td>-0.471974</td>\n",
+       "      <td>-0.257314</td>\n",
+       "      <td>0.102539</td>\n",
+       "      <td>-0.315415</td>\n",
+       "      <td>0.122501</td>\n",
+       "      <td>0.329387</td>\n",
+       "      <td>-0.484986</td>\n",
+       "      <td>0.351573</td>\n",
+       "      <td>0.432368</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1053 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      0         1         2         3         4         5   \\\n",
+       "test/20368     -6.737473  3.506645  1.100792  5.476943 -3.338335  0.276633   \n",
+       "test/20221     -6.560338  3.352505  0.892895  4.764644 -3.183715  0.292796   \n",
+       "test/20442     -6.648391  3.095893  0.785576  5.412939 -3.564779 -0.057957   \n",
+       "test/20800     -6.391430  1.779750  0.462306  2.715431 -2.103771 -1.989185   \n",
+       "training/3971  -5.361574  0.622695  0.208257 -3.572732  0.091671 -1.721061   \n",
+       "...                  ...       ...       ...       ...       ...       ...   \n",
+       "training/1134   0.129688  0.868474  0.158408  0.148159  0.302053  0.617617   \n",
+       "training/4956   0.207729 -0.256361 -0.847864  0.055823 -0.062639 -0.489831   \n",
+       "training/11154  0.989873  0.660073  0.298057  0.014688  0.094849  0.229320   \n",
+       "training/5914   0.597600 -0.274929 -0.519920  0.653873  0.242664  0.253243   \n",
+       "training/554    0.296712  0.237102  0.165965  0.044456  0.055577 -0.053818   \n",
+       "\n",
+       "                      6         7         8         9         10        11  \\\n",
+       "test/20368     -1.078069  2.903708  1.336813 -3.476732  1.427167 -0.266681   \n",
+       "test/20221     -1.101001  3.044708  2.059587 -3.483480  1.353684 -0.584850   \n",
+       "test/20442     -0.688464  2.905470  1.557411 -3.210068  0.827968 -0.532490   \n",
+       "test/20800     -1.984643  0.105770  4.286595 -3.886077 -0.450857  0.282356   \n",
+       "training/3971   1.046673  2.048406  4.613693 -1.404773  2.801529  2.242345   \n",
+       "...                  ...       ...       ...       ...       ...       ...   \n",
+       "training/1134  -1.176051 -0.358285 -0.874892 -0.830270 -1.113515 -0.046454   \n",
+       "training/4956   0.296728  0.145687 -1.076312 -0.194407 -0.076653  0.206450   \n",
+       "training/11154  0.049907  0.450760 -0.672756 -0.095832  0.664670  0.030931   \n",
+       "training/5914  -0.371124  0.603747 -0.012600  0.302335 -0.660933 -0.391477   \n",
+       "training/554    0.435192  0.170955 -0.636491  0.638219  0.501777 -0.471974   \n",
+       "\n",
+       "                      12        13        14        15        16        17  \\\n",
+       "test/20368     -0.471343  0.184137 -2.069631 -0.574260 -0.738087  3.788915   \n",
+       "test/20221     -0.158013  0.018590 -1.647612 -0.788695 -0.325296  3.511575   \n",
+       "test/20442      0.003652 -0.121509 -1.665959 -0.950425 -0.382220  4.117815   \n",
+       "test/20800     -1.321213  3.158543  0.123383 -0.627238  1.957629  3.836007   \n",
+       "training/3971   1.547014  6.425769 -2.543316 -0.761218  2.574573  5.359656   \n",
+       "...                  ...       ...       ...       ...       ...       ...   \n",
+       "training/1134   0.627627 -1.529335 -0.654220 -0.372447  0.006526 -0.608662   \n",
+       "training/4956  -0.603945  0.863203  0.882457  0.478035 -0.592900 -1.803185   \n",
+       "training/11154  0.212077  0.508668 -0.015358 -0.273089 -0.043678 -0.976209   \n",
+       "training/5914   0.386280  0.251975  0.099648  0.507324  0.229800 -1.581192   \n",
+       "training/554   -0.257314  0.102539 -0.315415  0.122501  0.329387 -0.484986   \n",
+       "\n",
+       "                      18        19  \n",
+       "test/20368     -1.683529 -4.035486  \n",
+       "test/20221     -1.597107 -3.987545  \n",
+       "test/20442     -1.906440 -3.899600  \n",
+       "test/20800     -2.467475 -0.543244  \n",
+       "training/3971  -1.035850 -3.710101  \n",
+       "...                  ...       ...  \n",
+       "training/1134   0.442872 -0.374222  \n",
+       "training/4956   0.490716  1.043012  \n",
+       "training/11154  0.072014  0.067489  \n",
+       "training/5914  -0.340255 -0.109365  \n",
+       "training/554    0.351573  0.432368  \n",
+       "\n",
+       "[1053 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(embeddings.vectors, index=embeddings.index2word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: \n",
+      "Type: Graph\n",
+      "Number of nodes: 25931\n",
+      "Number of edges: 100712\n",
+      "Average degree:   7.7677\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nx.info(G))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extracting the embeddings to be used in shallow-learning models. Please do vary the *dimensions* and the *window* parameters to generate multiple combination to be cross-validated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "Path(\"./embeddings\").mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from node2vec import Node2Vec\n",
+    "\n",
+    "dimensions = 10\n",
+    "window = 20\n",
+    "\n",
+    "node2vec = Node2Vec(G, dimensions=dimensions, num_walks=10, workers=4, quiet=True) \n",
+    "model = node2vec.fit(window=window) \n",
+    "embeddings = model.wv \n",
+    "\n",
+    "pd.DataFrame(embeddings.vectors, index=embeddings.index2word)\\\n",
+    "    .to_pickle(f\"./embeddings/bipartiteGraphEmbeddings_{dimensions}_{window}.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap8",
+   "language": "python",
+   "name": "chap8"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter07/02_supervised_classification-embeddings.ipynb b/Chapter08/02_supervised_classification-embeddings.ipynb
similarity index 76%
rename from Chapter07/02_supervised_classification-embeddings.ipynb
rename to Chapter08/02_supervised_classification-embeddings.ipynb
index 254c806..596f3f9 100644
--- a/Chapter07/02_supervised_classification-embeddings.ipynb
+++ b/Chapter08/02_supervised_classification-embeddings.ipynb
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,21 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./bipartiteGraphEmbeddings_20_10.p',\n",
-       " './bipartiteGraphEmbeddings_10.p',\n",
-       " './bipartiteGraphEmbeddings_20_30.p',\n",
-       " './bipartiteGraphEmbeddings_20.p',\n",
-       " './bipartiteGraphEmbeddings_20_20.p',\n",
-       " './bipartiteGraphEmbeddings_30.p']"
+       "['./embeddings/bipartiteGraphEmbeddings_10_20.p']"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -288,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,16 +325,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/sklearn/model_selection/_search.py:921: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
-      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
-      "  category=UserWarning\n"
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/sklearn/model_selection/_search.py:918: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan]\n",
+      "  warnings.warn(\n",
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/sklearn/model_selection/_search.py:931: RuntimeWarning: invalid value encountered in cast\n",
+      "  results[\"rank_%s\" % key_name] = np.asarray(\n"
      ]
     }
    ],
@@ -349,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -357,22 +353,17 @@
       "text/plain": [
        "GridSearchCV(cv=5,\n",
        "             estimator=Pipeline(steps=[('embeddings',\n",
-       "                                        EmbeddingsTransformer(embeddings_file='bipartiteGraphEmbeddings_20.p')),\n",
+       "                                        EmbeddingsTransformer(embeddings_file='./embeddings/bipartiteGraphEmbeddings_10_20.p')),\n",
        "                                       ('model',\n",
-       "                                        MultiOutputClassifier(estimator=RandomForestClassifier(class_weight='balanced')))]),\n",
+       "                                        MultiOutputClassifier(estimator=RandomForestClassifier()))]),\n",
        "             n_jobs=-1,\n",
-       "             param_grid={'embeddings__embeddings_file': ['./bipartiteGraphEmbeddings_20_10.p',\n",
-       "                                                         './bipartiteGraphEmbeddings_10.p',\n",
-       "                                                         './bipartiteGraphEmbeddings_20_30.p',\n",
-       "                                                         './bipartiteGraphEmbeddings_20.p',\n",
-       "                                                         './bipartiteGraphEmbeddings_20_20.p',\n",
-       "                                                         './bipartiteGraphEmbeddings_30.p'],\n",
+       "             param_grid={'embeddings__embeddings_file': ['./embeddings/bipartiteGraphEmbeddings_10_20.p'],\n",
        "                         'model__estimator__max_features': [0.2, 0.3, 'auto'],\n",
        "                         'model__estimator__n_estimators': [50, 100]},\n",
-       "             scoring=<function <lambda> at 0x14af7ee60>)"
+       "             scoring=<function <lambda> at 0x7f5fd7adf3a0>)"
       ]
      },
-     "execution_count": 259,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,18 +374,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'embeddings__embeddings_file': './bipartiteGraphEmbeddings_20_10.p',\n",
+       "{'embeddings__embeddings_file': './embeddings/bipartiteGraphEmbeddings_10_20.p',\n",
        " 'model__estimator__max_features': 0.2,\n",
        " 'model__estimator__n_estimators': 50}"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -436,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -445,16 +436,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6702547542160029"
+       "0.7072120559741657"
       ]
      },
-     "execution_count": 264,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -474,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -483,21 +474,21 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.97      0.94      0.95      1087\n",
-      "           1       0.93      0.74      0.83       719\n",
-      "           2       0.79      0.45      0.57       179\n",
-      "           3       0.96      0.64      0.77       149\n",
-      "           4       0.95      0.59      0.73       189\n",
-      "           5       0.95      0.45      0.61       117\n",
-      "           6       0.87      0.41      0.56       131\n",
-      "           7       0.83      0.21      0.34        89\n",
-      "           8       0.69      0.34      0.45        71\n",
-      "           9       0.61      0.25      0.35        56\n",
+      "           0       0.96      0.94      0.95      1087\n",
+      "           1       0.94      0.83      0.88       719\n",
+      "           2       0.77      0.67      0.72       179\n",
+      "           3       0.93      0.71      0.81       149\n",
+      "           4       0.89      0.67      0.76       189\n",
+      "           5       0.83      0.43      0.56       117\n",
+      "           6       0.79      0.44      0.56       131\n",
+      "           7       0.88      0.33      0.48        89\n",
+      "           8       0.76      0.44      0.55        71\n",
+      "           9       0.61      0.20      0.30        56\n",
       "\n",
-      "   micro avg       0.94      0.72      0.81      2787\n",
-      "   macro avg       0.85      0.50      0.62      2787\n",
-      "weighted avg       0.92      0.72      0.79      2787\n",
-      " samples avg       0.76      0.75      0.75      2787\n",
+      "   micro avg       0.92      0.77      0.84      2787\n",
+      "   macro avg       0.84      0.56      0.66      2787\n",
+      "weighted avg       0.91      0.77      0.83      2787\n",
+      " samples avg       0.81      0.80      0.80      2787\n",
       "\n"
      ]
     },
@@ -505,7 +496,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/deusebio/.pyenv/versions/3.7.6/envs/ml-book-7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
+      "/home/deusebio/.pyenv/versions/graph-machine-learning/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
       "  _warn_prf(average, modifier, msg_start, len(result))\n"
      ]
     }
@@ -513,13 +504,20 @@
    "source": [
     "print(classification_report(labels, preds))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ml-book-7",
+   "display_name": "chap8",
    "language": "python",
-   "name": "ml-book-7"
+   "name": "chap8"
   },
   "language_info": {
    "codemirror_mode": {
@@ -531,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.14"
   }
  },
  "nbformat": 4,
diff --git a/Chapter08/03_supervised_classification_graphSAGE-TFIDF.ipynb b/Chapter08/03_supervised_classification_graphSAGE-TFIDF.ipynb
new file mode 100644
index 0000000..94ee104
--- /dev/null
+++ b/Chapter08/03_supervised_classification_graphSAGE-TFIDF.ipynb
@@ -0,0 +1,1712 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph Neural Network Topic Classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following we will focus on building a model for topic classification based on a Graph Neural Network approach.\n",
+    "\n",
+    "In particular in the following we will show you how to:\n",
+    "\n",
+    "* Create a TF-IDF representation of the corpus, that will be used as node features in the Graph Neural Network model \n",
+    "* Build, train a Graph Neural Network model and identify the best threshold for classifying documents \n",
+    "* Test the performance of the model in a out-of-sample tests, following a truly inductive approach \n",
+    "\n",
+    "**NOTE: This Notebook can only be run after the 01_nlp_graph_creation notebook, as some of the results computed in the first notebook will be here reused.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nltk "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = pd.read_pickle(\"corpus.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clean_text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>language</th>\n",
+       "      <th>parsed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...</td>\n",
+       "      <td>[trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...</td>\n",
+       "      <td>[grain]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14829</th>\n",
+       "      <td>JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...</td>\n",
+       "      <td>[crude, nat-gas]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...</td>\n",
+       "      <td>[corn, grain, rice, rubber, sugar, tin, trade]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14833</th>\n",
+       "      <td>INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...</td>\n",
+       "      <td>[palm-oil, veg-oil]</td>\n",
+       "      <td>en</td>\n",
+       "      <td>(INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   clean_text  \\\n",
+       "id                                                              \n",
+       "test/14826  ASIAN EXPORTERS FEAR DAMAGE FROM U.S.-JAPAN RI...   \n",
+       "test/14828  CHINA DAILY SAYS VERMIN EAT 7-12 PCT GRAIN STO...   \n",
+       "test/14829  JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWA...   \n",
+       "test/14832  THAI TRADE DEFICIT WIDENS IN FIRST QUARTER  Th...   \n",
+       "test/14833  INDONESIA SEES CPO PRICE RISING SHARPLY  Indon...   \n",
+       "\n",
+       "                                                     label language  \\\n",
+       "id                                                                    \n",
+       "test/14826                                         [trade]       en   \n",
+       "test/14828                                         [grain]       en   \n",
+       "test/14829                                [crude, nat-gas]       en   \n",
+       "test/14832  [corn, grain, rice, rubber, sugar, tin, trade]       en   \n",
+       "test/14833                             [palm-oil, veg-oil]       en   \n",
+       "\n",
+       "                                                       parsed  \n",
+       "id                                                             \n",
+       "test/14826  (ASIAN, EXPORTERS, FEAR, DAMAGE, FROM, U.S.-JA...  \n",
+       "test/14828  (CHINA, DAILY, SAYS, VERMIN, EAT, 7, -, 12, PC...  \n",
+       "test/14829  (JAPAN, TO, REVISE, LONG, -, TERM, ENERGY, DEM...  \n",
+       "test/14832  (THAI, TRADE, DEFICIT, WIDENS, IN, FIRST, QUAR...  \n",
+       "test/14833  (INDONESIA, SEES, CPO, PRICE, RISING, SHARPLY,...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "topics = Counter([label for document_labels in corpus[\"label\"] for label in document_labels]).most_common(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('earn', 3964),\n",
+       " ('acq', 2369),\n",
+       " ('money-fx', 717),\n",
+       " ('grain', 582),\n",
+       " ('crude', 578),\n",
+       " ('trade', 485),\n",
+       " ('interest', 478),\n",
+       " ('ship', 286),\n",
+       " ('wheat', 283),\n",
+       " ('corn', 237)]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topicsList = [topic[0] for topic in topics]\n",
+    "topicsSet = set(topicsList)\n",
+    "dataset = corpus[corpus[\"label\"].apply(lambda x: len(topicsSet.intersection(x))>0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_labels(corpus, topicsList=topicsList):\n",
+    "    return corpus[\"label\"].apply(\n",
+    "        lambda labels: pd.Series({label: 1 for label in labels}).reindex(topicsList).fillna(0)\n",
+    "    )[topicsList]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = get_labels(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>earn</th>\n",
+       "      <th>acq</th>\n",
+       "      <th>money-fx</th>\n",
+       "      <th>grain</th>\n",
+       "      <th>crude</th>\n",
+       "      <th>trade</th>\n",
+       "      <th>interest</th>\n",
+       "      <th>ship</th>\n",
+       "      <th>wheat</th>\n",
+       "      <th>corn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>test/14826</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>test/14828</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <th>test/14829</th>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>test/14832</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/14839</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            earn  acq  money-fx  grain  crude  trade  interest  ship  wheat  \\\n",
+       "id                                                                            \n",
+       "test/14826   0.0  0.0       0.0    0.0    0.0    1.0       0.0   0.0    0.0   \n",
+       "test/14828   0.0  0.0       0.0    1.0    0.0    0.0       0.0   0.0    0.0   \n",
+       "test/14829   0.0  0.0       0.0    0.0    1.0    0.0       0.0   0.0    0.0   \n",
+       "test/14832   0.0  0.0       0.0    1.0    0.0    1.0       0.0   0.0    0.0   \n",
+       "test/14839   0.0  0.0       0.0    0.0    0.0    0.0       0.0   1.0    0.0   \n",
+       "\n",
+       "            corn  \n",
+       "id                \n",
+       "test/14826   0.0  \n",
+       "test/14828   0.0  \n",
+       "test/14829   0.0  \n",
+       "test/14832   1.0  \n",
+       "test/14839   0.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features(corpus):\n",
+    "    return corpus[\"parsed\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features_and_labels(corpus):\n",
+    "    return get_features(corpus), get_labels(corpus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test_split(corpus):\n",
+    "    train_idx = [idx for idx in corpus.index if \"training/\" in idx]\n",
+    "    test_idx = [idx for idx in corpus.index if \"test/\" in idx]\n",
+    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_spacy_tokenizer(pos_filter=[\"NOUN\", \"VERB\", \"PROPN\"]):\n",
+    "    def tokenizer(doc):\n",
+    "        return [token.lemma_ for token in doc if (pos_filter is None) or (token.pos_ in pos_filter)] \n",
+    "    return tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cntVectorizer = TfidfVectorizer(\n",
+    "    analyzer=my_spacy_tokenizer(),\n",
+    "    max_df = 0.25, min_df = 2, max_features = 10000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainFeatures, _ = get_features_and_labels(train)\n",
+    "testFeatures, _ = get_features_and_labels(test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainedTransformed = cntVectorizer.fit_transform(trainFeatures)\n",
+    "testTransformed = cntVectorizer.transform(testFeatures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = pd.concat([\n",
+    "    pd.DataFrame.sparse.from_spmatrix(trainedTransformed, index=trainFeatures.index), \n",
+    "    pd.DataFrame.sparse.from_spmatrix(testTransformed, index=testFeatures.index)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9034, 10000)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating the Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-11-16 22:50:43.187158: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2024-11-16 22:50:43.187173: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "2024-11-16 22:50:46.819716: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2024-11-16 22:50:46.819732: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2024-11-16 22:50:46.819742: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (pelican): /proc/driver/nvidia/version does not exist\n",
+      "2024-11-16 22:50:46.820221: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import stellargraph as sg\n",
+    "from stellargraph import StellarGraph, IndexedArray\n",
+    "from stellargraph.mapper import GraphSAGENodeGenerator\n",
+    "from stellargraph.layer import GraphSAGE\n",
+    "\n",
+    "from tensorflow.keras import layers, optimizers, losses, metrics, Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = pd.read_pickle(\"bipartiteEdges.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityTypes = {entity: ith for ith, entity in enumerate(edges[\"type\"].unique())}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'keywords': 0, 'GPE': 1, 'ORG': 2, 'PERSON': 3}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "entityTypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documentFeatures = features.loc[list(set(corpus.index).intersection(features.index))] #.assign(document=1, entity=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>9990</th>\n",
+       "      <th>9991</th>\n",
+       "      <th>9992</th>\n",
+       "      <th>9993</th>\n",
+       "      <th>9994</th>\n",
+       "      <th>9995</th>\n",
+       "      <th>9996</th>\n",
+       "      <th>9997</th>\n",
+       "      <th>9998</th>\n",
+       "      <th>9999</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>training/9850</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/6208</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/18325</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/859</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/128</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 10000 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               0     1     2     3     4     5     6     7     8     9     \\\n",
+       "id                                                                          \n",
+       "training/9850   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
+       "training/6208   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
+       "test/18325      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
+       "training/859    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
+       "training/128    0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
+       "\n",
+       "               ...  9990  9991  9992  9993  9994  9995  9996  9997  9998  9999  \n",
+       "id             ...                                                              \n",
+       "training/9850  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
+       "training/6208  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
+       "test/18325     ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
+       "training/859   ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
+       "training/128   ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
+       "\n",
+       "[5 rows x 10000 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documentFeatures.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entities = edges.groupby([\"target\", \"type\"])[\"source\"].count().groupby(level=0).apply(\n",
+    "    lambda s: s.droplevel(0).reindex(entityTypes.keys()).fillna(0)\n",
+    ").unstack(level=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityFeatures = (entities.T / entities.sum(axis=1)).T.assign(document=0, entity=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = {\"entity\": entityFeatures, \n",
+    "         \"document\": documentFeatures}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stellarGraph = StellarGraph(nodes, \n",
+    "                            edges[edges[\"source\"].isin(documentFeatures.index)], \n",
+    "                            edge_type_column=\"type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 24177, Edges: 87658\n",
+      "\n",
+      " Node types:\n",
+      "  entity: [15143]\n",
+      "    Features: float32 vector, length 6\n",
+      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
+      "  document: [9034]\n",
+      "    Features: float32 vector, length 10000\n",
+      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
+      "\n",
+      " Edge types:\n",
+      "    document-keywords->entity: [78828]\n",
+      "        Weights: range=[0.0827011, 1], mean=0.258479, std=0.0898449\n",
+      "        Features: none\n",
+      "    document-ORG->entity: [4275]\n",
+      "        Weights: range=[2, 24], mean=3.33427, std=2.38695\n",
+      "        Features: none\n",
+      "    document-GPE->entity: [3141]\n",
+      "        Weights: range=[2, 26], mean=3.1958, std=2.03227\n",
+      "        Features: none\n",
+      "    document-PERSON->entity: [1414]\n",
+      "        Weights: range=[2, 18], mean=3.17327, std=1.97911\n",
+      "        Features: none\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stellarGraph.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stellargraph.data import EdgeSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "splitter = EdgeSplitter(stellarGraph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** Sampled 17531 positive and 17531 negative edges. **\n"
+     ]
+    }
+   ],
+   "source": [
+    "graphTest, samplesTest, labelsTest = splitter.train_test_split(p=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 24177, Edges: 87658\n",
+      "\n",
+      " Node types:\n",
+      "  entity: [15143]\n",
+      "    Features: float32 vector, length 6\n",
+      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
+      "  document: [9034]\n",
+      "    Features: float32 vector, length 10000\n",
+      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
+      "\n",
+      " Edge types:\n",
+      "    document-keywords->entity: [78828]\n",
+      "        Weights: range=[0.0827011, 1], mean=0.258479, std=0.0898449\n",
+      "        Features: none\n",
+      "    document-ORG->entity: [4275]\n",
+      "        Weights: range=[2, 24], mean=3.33427, std=2.38695\n",
+      "        Features: none\n",
+      "    document-GPE->entity: [3141]\n",
+      "        Weights: range=[2, 26], mean=3.1958, std=2.03227\n",
+      "        Features: none\n",
+      "    document-PERSON->entity: [1414]\n",
+      "        Weights: range=[2, 18], mean=3.17327, std=1.97911\n",
+      "        Features: none\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stellarGraph.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 24177, Edges: 70127\n",
+      "\n",
+      " Node types:\n",
+      "  entity: [15143]\n",
+      "    Features: float32 vector, length 6\n",
+      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
+      "  document: [9034]\n",
+      "    Features: float32 vector, length 10000\n",
+      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
+      "\n",
+      " Edge types:\n",
+      "    document-keywords->entity: [63078]\n",
+      "        Weights: range=[0.0827011, 1], mean=0.258399, std=0.0897861\n",
+      "        Features: none\n",
+      "    document-ORG->entity: [3404]\n",
+      "        Weights: range=[2, 22], mean=3.31463, std=2.35368\n",
+      "        Features: none\n",
+      "    document-GPE->entity: [2529]\n",
+      "        Weights: range=[2, 26], mean=3.21669, std=2.04549\n",
+      "        Features: none\n",
+      "    document-PERSON->entity: [1116]\n",
+      "        Weights: range=[2, 18], mean=3.18907, std=2.03272\n",
+      "        Features: none\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(graphTest.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Topic Classification Model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by splitting the data into train, validation and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets = labels.reindex(documentFeatures.index).fillna(0)\n",
+    "#documentFeatures.drop([\"entity\", \"document\"], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>earn</th>\n",
+       "      <th>acq</th>\n",
+       "      <th>money-fx</th>\n",
+       "      <th>grain</th>\n",
+       "      <th>crude</th>\n",
+       "      <th>trade</th>\n",
+       "      <th>interest</th>\n",
+       "      <th>ship</th>\n",
+       "      <th>wheat</th>\n",
+       "      <th>corn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>training/9850</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/6208</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test/18325</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/859</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>training/128</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               earn  acq  money-fx  grain  crude  trade  interest  ship  \\\n",
+       "id                                                                        \n",
+       "training/9850   0.0  1.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
+       "training/6208   0.0  0.0       0.0    0.0    1.0    0.0       0.0   0.0   \n",
+       "test/18325      0.0  1.0       0.0    0.0    1.0    0.0       0.0   0.0   \n",
+       "training/859    1.0  0.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
+       "training/128    0.0  1.0       0.0    0.0    0.0    0.0       0.0   0.0   \n",
+       "\n",
+       "               wheat  corn  \n",
+       "id                          \n",
+       "training/9850    0.0   0.0  \n",
+       "training/6208    0.0   0.0  \n",
+       "test/18325       0.0   0.0  \n",
+       "training/859     0.0   0.0  \n",
+       "training/128     0.0   0.0  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "targets.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test_split(corpus):\n",
+    "    graphIndex = [index for index in corpus.index]\n",
+    "    \n",
+    "    train_idx = [idx for idx in graphIndex if \"training/\" in idx]\n",
+    "    test_idx = [idx for idx in graphIndex if \"test/\" in idx]\n",
+    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled, hold_out = train_test_split(targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allNeighbors = np.unique([n for node in sampled.index for n in stellarGraph.neighbors(node)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subgraph = stellarGraph.subgraph(set(sampled.index).union(allNeighbors))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StellarGraph: Undirected multigraph\n",
+      " Nodes: 17075, Edges: 63031\n",
+      "\n",
+      " Node types:\n",
+      "  entity: [10586]\n",
+      "    Features: float32 vector, length 6\n",
+      "    Edge types: entity-GPE->document, entity-ORG->document, entity-PERSON->document, entity-keywords->document\n",
+      "  document: [6489]\n",
+      "    Features: float32 vector, length 10000\n",
+      "    Edge types: document-GPE->entity, document-ORG->entity, document-PERSON->entity, document-keywords->entity\n",
+      "\n",
+      " Edge types:\n",
+      "    document-keywords->entity: [56639]\n",
+      "        Weights: range=[0.0918226, 1], mean=0.257404, std=0.0887759\n",
+      "        Features: none\n",
+      "    document-ORG->entity: [3126]\n",
+      "        Weights: range=[2, 22], mean=3.30742, std=2.29417\n",
+      "        Features: none\n",
+      "    document-GPE->entity: [2230]\n",
+      "        Weights: range=[2, 26], mean=3.23767, std=2.07487\n",
+      "        Features: none\n",
+      "    document-PERSON->entity: [1036]\n",
+      "        Weights: range=[2, 18], mean=3.17664, std=2.04459\n",
+      "        Features: none\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(subgraph.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train, leftOut = train_test_split(\n",
+    "    sampled,\n",
+    "    train_size=0.1,\n",
+    "    test_size=None,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "validation, test = train_test_split(\n",
+    "    leftOut, train_size=0.2, test_size=None, random_state=100,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation = validation[validation.sum(axis=1) > 0]\n",
+    "test = test[test.sum(axis=1) > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation: (1168, 10)\n",
+      "Test: (4673, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Validation: {validation.shape}\")\n",
+    "print(f\"Test: {test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start  by creating the model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 50\n",
+    "num_samples = [10, 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stellargraph.mapper import HinSAGENodeGenerator\n",
+    "\n",
+    "generator = HinSAGENodeGenerator(subgraph, batch_size, num_samples, head_node_type=\"document\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stellargraph.layer import HinSAGE\n",
+    "\n",
+    "graphsage_model = HinSAGE(\n",
+    "    layer_sizes=[32, 32], generator=generator, bias=True, dropout=0.5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_inp, x_out = graphsage_model.in_out_tensors()\n",
+    "prediction = layers.Dense(units=train.shape[1], activation=\"sigmoid\")(x_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TensorShape([None, 10])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Model(inputs=x_inp, outputs=prediction)\n",
+    "model.compile(\n",
+    "    optimizer=optimizers.Adam(learning_rate=0.005),\n",
+    "    loss=losses.binary_crossentropy,\n",
+    "    metrics=[\"acc\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now train the model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_gen = generator.flow(train.index, train, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_gen = generator.flow(validation.index, validation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "13/13 [==============================] - 66s 5s/step - loss: 0.6189 - acc: 0.2809 - val_loss: 0.5097 - val_acc: 0.4486\n",
+      "Epoch 2/50\n",
+      "13/13 [==============================] - 67s 5s/step - loss: 0.4761 - acc: 0.4630 - val_loss: 0.4201 - val_acc: 0.4486\n",
+      "Epoch 3/50\n",
+      "13/13 [==============================] - 62s 5s/step - loss: 0.3971 - acc: 0.4599 - val_loss: 0.3610 - val_acc: 0.4486\n",
+      "Epoch 4/50\n",
+      "13/13 [==============================] - 61s 5s/step - loss: 0.3475 - acc: 0.4599 - val_loss: 0.3228 - val_acc: 0.4486\n",
+      "Epoch 5/50\n",
+      "13/13 [==============================] - 72s 6s/step - loss: 0.3132 - acc: 0.4676 - val_loss: 0.2949 - val_acc: 0.4486\n",
+      "Epoch 6/50\n",
+      "13/13 [==============================] - 73s 6s/step - loss: 0.2871 - acc: 0.5293 - val_loss: 0.2715 - val_acc: 0.4983\n",
+      "Epoch 7/50\n",
+      "13/13 [==============================] - 62s 5s/step - loss: 0.2663 - acc: 0.6173 - val_loss: 0.2513 - val_acc: 0.6310\n",
+      "Epoch 8/50\n",
+      "13/13 [==============================] - 69s 5s/step - loss: 0.2468 - acc: 0.6836 - val_loss: 0.2348 - val_acc: 0.6524\n",
+      "Epoch 9/50\n",
+      "13/13 [==============================] - 73s 6s/step - loss: 0.2309 - acc: 0.7130 - val_loss: 0.2211 - val_acc: 0.6892\n",
+      "Epoch 10/50\n",
+      "13/13 [==============================] - 71s 6s/step - loss: 0.2156 - acc: 0.7392 - val_loss: 0.2096 - val_acc: 0.7397\n",
+      "Epoch 11/50\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(\n",
+    "    train_gen, epochs=50, validation_data=val_gen, verbose=1, shuffle=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sg.utils.plot_history(history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history = model.fit(\n",
+    "    train_gen, epochs=50, validation_data=val_gen, verbose=1, shuffle=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sg.utils.plot_history(history)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Threshold identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_gen = generator.flow(test.index, test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_metrics = model.evaluate(test_gen)\n",
+    "print(\"\\nTest Set Metrics:\")\n",
+    "for name, val in zip(model.metrics_names, test_metrics):\n",
+    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_predictions = pd.DataFrame(model.predict(test_gen), index=test.index, columns=test.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_results = pd.concat({\n",
+    "    \"target\": test, \n",
+    "    \"preds\": test_predictions\n",
+    "}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f1s = {}\n",
+    "\n",
+    "for th in [0.01,0.05,0.1,0.2,0.3,0.4,0.5]:\n",
+    "    f1s[th] = f1_score(test_results[\"target\"], 1.0*(test_results[\"preds\"]>th), average=\"macro\")\n",
+    "    \n",
+    "pd.Series(f1s).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As it can be seen, with a threshold of about 0.2 we obtain the best performances. We thus use this value for producing the classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(test_results[\"target\"], 1.0*(test_results[\"preds\"]>0.2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inductive Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now provide a prediction truly inductive, thus we will be using the full graph and we will also use the threshold of 0.2 we have identified above as the one providing the top f1-score.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generator = HinSAGENodeGenerator(stellarGraph, batch_size, num_samples, head_node_type=\"document\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hold_out = hold_out[hold_out.sum(axis=1) > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hold_out_gen = generator.flow(hold_out.index, hold_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hold_out_predictions = model.predict(hold_out_gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = pd.DataFrame(1.0*(hold_out_predictions > 0.2), index=hold_out.index, columns=hold_out.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = pd.concat({\n",
+    "    \"target\": hold_out, \n",
+    "    \"preds\": preds\n",
+    "}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(results[\"target\"], results[\"preds\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap8",
+   "language": "python",
+   "name": "chap8"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter08/04_supervised_classification_pyg.ipynb b/Chapter08/04_supervised_classification_pyg.ipynb
new file mode 100644
index 0000000..ea1ae43
--- /dev/null
+++ b/Chapter08/04_supervised_classification_pyg.ipynb
@@ -0,0 +1,911 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph Neural Network Topic Classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following we will focus on building a model for topic classification based on a Graph Neural Network approach.\n",
+    "\n",
+    "In particular in the following we will show you how to:\n",
+    "\n",
+    "* Create a TF-IDF representation of the corpus, that will be used as node features in the Graph Neural Network model \n",
+    "* Build, train a Graph Neural Network model and identify the best threshold for classifying documents \n",
+    "\n",
+    "**NOTE: This Notebook can only be run after the 01_nlp_graph_creation notebook, as some of the results computed in the first notebook will be here reused.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus = pd.read_pickle(\"corpus.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corpus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "topics = Counter([label for document_labels in corpus[\"label\"] for label in document_labels]).most_common(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topicsList = [topic[0] for topic in topics]\n",
+    "topicsSet = set(topicsList)\n",
+    "dataset = corpus[corpus[\"label\"].apply(lambda x: len(topicsSet.intersection(x))>0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_labels(corpus, topicsList=topicsList):\n",
+    "    return corpus[\"label\"].apply(\n",
+    "        lambda labels: pd.Series({label: 1 for label in labels}).reindex(topicsList).fillna(0)\n",
+    "    )[topicsList]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = get_labels(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "labels.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features(corpus):\n",
+    "    return corpus[\"parsed\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features_and_labels(corpus):\n",
+    "    return get_features(corpus), get_labels(corpus)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test_split(corpus):\n",
+    "    train_idx = [idx for idx in corpus.index if \"training/\" in idx]\n",
+    "    test_idx = [idx for idx in corpus.index if \"test/\" in idx]\n",
+    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_spacy_tokenizer(pos_filter=[\"NOUN\", \"VERB\", \"PROPN\"]):\n",
+    "    def tokenizer(doc):\n",
+    "        return [token.lemma_ for token in doc if (pos_filter is None) or (token.pos_ in pos_filter)] \n",
+    "    return tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cntVectorizer = TfidfVectorizer(\n",
+    "    analyzer=my_spacy_tokenizer(),\n",
+    "    max_df = 0.25, min_df = 2, max_features = 10000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainFeatures, _ = get_features_and_labels(train)\n",
+    "testFeatures, _ = get_features_and_labels(test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainedTransformed = cntVectorizer.fit_transform(trainFeatures)\n",
+    "testTransformed = cntVectorizer.transform(testFeatures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = pd.concat([\n",
+    "    pd.DataFrame.sparse.from_spmatrix(trainedTransformed, index=trainFeatures.index), \n",
+    "    pd.DataFrame.sparse.from_spmatrix(testTransformed, index=testFeatures.index)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating the Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.data import HeteroData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = pd.read_pickle(\"bipartiteEdges.p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityTypes = {entity: ith for ith, entity in enumerate(edges[\"type\"].unique())}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityTypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documentFeatures = features.loc[list(set(corpus.index).intersection(features.index))] #.assign(document=1, entity=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documentFeatures.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entities = edges.groupby([\"target\", \"type\"])[\"source\"].count().groupby(level=0).apply(\n",
+    "    lambda s: s.droplevel(0).reindex(entityTypes.keys()).fillna(0)\n",
+    ").unstack(level=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entityFeatures = (entities.T / entities.sum(axis=1)).T.assign(document=0, entity=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = {\"entity\": entityFeatures, \n",
+    "         \"document\": documentFeatures}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets = labels.reindex(documentFeatures.index).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test_split(corpus):\n",
+    "    graphIndex = [index for index in corpus.index]\n",
+    "    \n",
+    "    train_idx = [idx for idx in graphIndex if \"training/\" in idx]\n",
+    "    test_idx = [idx for idx in graphIndex if \"test/\" in idx]\n",
+    "    return corpus.loc[train_idx], corpus.loc[test_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled, hold_out = train_test_split(targets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train, leftOut = train_test_split(\n",
+    "    sampled,\n",
+    "    train_size=0.1,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "validation, test = train_test_split(\n",
+    "    leftOut, train_size=0.2, test_size=None, random_state=100,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = train[train.sum(axis=1) > 0]\n",
+    "validation = validation[validation.sum(axis=1) > 0]\n",
+    "test = test[test.sum(axis=1) > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Train: {train.shape}\")\n",
+    "print(f\"Validation: {validation.shape}\")\n",
+    "print(f\"Test: {test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_maps = {k: ith for ith, k in enumerate(documentFeatures.index)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ents_maps = {k: ith for ith, k in enumerate(entityFeatures.index)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labs_maps = {k: ith for ith, k in enumerate(labels.columns)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges[\"source_id\"] = edges[\"source\"].apply(lambda x: docs_maps.get(x, -1))\n",
+    "edges[\"target_id\"] = edges[\"target\"].apply(lambda x: ents_maps.get(x, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch_sparse\n",
+    "\n",
+    "def df_to_torch(df: pd.DataFrame):\n",
+    "    try:\n",
+    "        # @amarzullo: needs to be torch_sparse coo\n",
+    "        coo = df.sparse_to_coo()\n",
+    "        return torch_sparse.coalesce(coo.coords, coo.data, coo.shape)\n",
+    "        #coo = df.sparse.to_coo()\n",
+    "        #return torch.sparse_coo_tensor(coo.coords, coo.data, coo.shape) #.to_sparse_csr()\n",
+    "    except AttributeError:\n",
+    "        return torch.from_numpy(df.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = HeteroData()\n",
+    "\n",
+    "data[\"document\"].x = df_to_torch(documentFeatures)#.to_dense() #@amarzullo to_dense\n",
+    "data[\"entity\"].x = df_to_torch(entityFeatures)#.to_dense() #@amarzullo to_dense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _type, group in edges[(edges[\"source_id\"]!=-1) * (edges[\"target_id\"]!=-1)].groupby(\"type\"):\n",
+    "    data[(\"document\", _type, \"entity\")].edge_index = df_to_torch(group[[\"source_id\", \"target_id\"]].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"document\"].y = df_to_torch(targets).to(torch.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"document\"][\"train_mask\"] = df_to_torch(train.sum(axis=1).reindex(documentFeatures.index).fillna(0)).to(torch.bool)\n",
+    "data[\"document\"][\"val_mask\"] = df_to_torch(validation.sum(axis=1).reindex(documentFeatures.index).fillna(0)).to(torch.bool)\n",
+    "data[\"document\"][\"test_mask\"] = df_to_torch(test.sum(axis=1).reindex(documentFeatures.index).fillna(0)).to(torch.bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch_geometric.transforms as T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = T.ToUndirected()(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.nn import SAGEConv, to_hetero\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class GNN(torch.nn.Module):\n",
+    "    def __init__(self, hidden_channels, out_channels):\n",
+    "        super().__init__()\n",
+    "        self.conv1 = SAGEConv((-1, -1), hidden_channels)\n",
+    "        self.conv2 = SAGEConv((-1, -1), out_channels)\n",
+    "\n",
+    "    def forward(self, x, edge_index):\n",
+    "        x = x.float() #@amarzullo\n",
+    "        x = self.conv1(x, edge_index).relu()\n",
+    "        x = self.conv2(x, edge_index)\n",
+    "        return F.sigmoid(x)\n",
+    "\n",
+    "\n",
+    "model = GNN(hidden_channels=64, out_channels=len(labs_maps))\n",
+    "model = to_hetero(model, data.metadata(), aggr='sum')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():  # Initialize lazy modules.\n",
+    "    out = model(data.x_dict, data.edge_index_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cpu\")\n",
+    "\n",
+    "model = model.to(device)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "\n",
+    "@dataclass\n",
+    "class Accuracy:\n",
+    "    correct: int\n",
+    "    total: int\n",
+    "\n",
+    "    @property\n",
+    "    def score(self):\n",
+    "        return float(self.correct) * 1.0 / self.total\n",
+    "\n",
+    "    def __add__(self, other: 'Accuracy'):\n",
+    "        if not isinstance(other, Accuracy):\n",
+    "            raise ValueError(\"Cannot add objects other than Accuracy\")\n",
+    "\n",
+    "        return Accuracy(self.correct+other.correct, self.total+other.total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score(correct):\n",
+    "    return Accuracy(int(correct.sum()), int(np.prod(correct.shape)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def training(data, train_mask):\n",
+    "    model.train()\n",
+    "\n",
+    "    # zero the parameter gradients\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    out = model(data.x_dict, data.edge_index_dict)\n",
+    "        \n",
+    "    loss = F.binary_cross_entropy(out['document'][train_mask], data['document'].y[train_mask])\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    return float(loss)\n",
+    "\n",
+    "@torch.no_grad()\n",
+    "def eval(data, mask):\n",
+    "     # Test/Evaluate\n",
+    "    model.eval()\n",
+    "\n",
+    "    out = model(data.x_dict, data.edge_index_dict)[\"document\"][mask]\n",
+    "\n",
+    "    pred = (1.0*(out>0.5) == data[\"document\"].y[mask])\n",
+    "    \n",
+    "    return score(pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_mask = data['document'].train_mask\n",
+    "val_mask = data['document'].val_mask\n",
+    "\n",
+    "for epoch in range(10):  # loop over the dataset multiple times\n",
+    "\n",
+    "    loss = training(data, train_mask)\n",
+    "        \n",
+    "    # Test/Evaluate\n",
+    "    train_score, val_score = eval(data, train_mask), eval(data, val_mask)\n",
+    "\n",
+    "    print(f\"Epoch {epoch} => Training: {train_score.score} Validation: {val_score.score}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With batches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.loader import NeighborLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_input_nodes = ('document', data['document'].train_mask)\n",
+    "val_input_nodes = ('document', data['document'].val_mask)\n",
+    "kwargs = {'batch_size': 128}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loader = NeighborLoader(data, num_neighbors=[10] * 2, shuffle=True,\n",
+    "                                  input_nodes=train_input_nodes, **kwargs)\n",
+    "val_loader = NeighborLoader(data, num_neighbors=[10] * 2,\n",
+    "                                input_nodes=val_input_nodes, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_score = Accuracy(0, 0)\n",
+    "for nth, batch in enumerate(train_loader):\n",
+    "    batch_size = batch['document'].batch_size\n",
+    "    train_mask = range(batch_size)\n",
+    "    train_score += eval(batch, train_mask)\n",
+    "\n",
+    "val_score = Accuracy(0, 0)\n",
+    "for nth, batch in enumerate(val_loader):\n",
+    "    batch_size = batch['document'].batch_size\n",
+    "    train_mask = range(batch_size)\n",
+    "    val_score += eval(batch, train_mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for epoch in range(1):\n",
+    "    loss = 0\n",
+    "    for nth, batch in enumerate(train_loader):\n",
+    "        batch_size = batch['document'].batch_size\n",
+    "        train_mask = range(batch_size)\n",
+    "        loss += training(batch, train_mask)*batch_size\n",
+    "\n",
+    "    # Training error\n",
+    "    train_score = Accuracy(0, 0)\n",
+    "    for nth, batch in enumerate(train_loader):\n",
+    "        batch_size = batch['document'].batch_size\n",
+    "        train_mask = range(batch_size)\n",
+    "        train_score += eval(batch, train_mask)\n",
+    "\n",
+    "    val_score = Accuracy(0, 0)\n",
+    "    for nth, batch in enumerate(val_loader):\n",
+    "        batch_size = batch['document'].batch_size\n",
+    "        train_mask = range(batch_size)\n",
+    "        val_score += eval(batch, train_mask)\n",
+    "    \n",
+    "    print(f\"Epoch {epoch} => Loss: {loss} Train: {train_score.score} Val: {val_score.score}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Threshold identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_input_nodes = ('document', data['document'].test_mask)\n",
+    "kwargs = {'batch_size': 128}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_loader = NeighborLoader(data, num_neighbors=[10] * 2, input_nodes=test_input_nodes, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def get_output(data, mask):\n",
+    "     # Test/Evaluate\n",
+    "    model.eval()\n",
+    "\n",
+    "    out = model(data.x_dict, data.edge_index_dict)[\"document\"][mask]\n",
+    "\n",
+    "    return pd.DataFrame(out)\n",
+    "\n",
+    "def reindex(df, indices):\n",
+    "    df.index = indices\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remap_index(df, docs_maps, labs_maps):\n",
+    "    inv_docs_maps = {v:k for k, v in docs_maps.items()}\n",
+    "    inv_labs_maps = {v:k for k, v in labs_maps.items()}\n",
+    "    \n",
+    "    df.index = [inv_docs_maps[x] for x in df.index]\n",
+    "    df.columns = [inv_labs_maps[x] for x in df.columns]\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = []\n",
+    "for nth, batch in enumerate(test_loader):\n",
+    "    batch_size = batch['document'].batch_size\n",
+    "    train_mask = range(batch_size)\n",
+    "    preds.append(\n",
+    "        remap_index(\n",
+    "            reindex(\n",
+    "                get_output(batch, train_mask), \n",
+    "                batch[\"document\"].input_id.tolist()\n",
+    "            ),\n",
+    "            docs_maps,\n",
+    "            labs_maps\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_predictions = pd.concat(preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_results = pd.concat({\n",
+    "    \"target\": test, \n",
+    "    \"preds\": test_predictions\n",
+    "}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f1s = {}\n",
+    "\n",
+    "for th in [0.01,0.05,0.1,0.2,0.3,0.4,0.5]:\n",
+    "    f1s[th] = f1_score(test_results[\"target\"], 1.0*(test_results[\"preds\"]>th), average=\"macro\")\n",
+    "    \n",
+    "pd.Series(f1s).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(classification_report(test_results[\"target\"], 1.0*(test_results[\"preds\"]>0.2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap8",
+   "language": "python",
+   "name": "chap8"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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+    {file = "yarl-1.15.2-cp39-cp39-win32.whl", hash = "sha256:5b48388ded01f6f2429a8c55012bdbd1c2a0c3735b3e73e221649e524c34a58d"},
+    {file = "yarl-1.15.2-cp39-cp39-win_amd64.whl", hash = "sha256:81dadafb3aa124f86dc267a2168f71bbd2bfb163663661ab0038f6e4b8edb810"},
+    {file = "yarl-1.15.2-py3-none-any.whl", hash = "sha256:0d3105efab7c5c091609abacad33afff33bdff0035bece164c98bcf5a85ef90a"},
+    {file = "yarl-1.15.2.tar.gz", hash = "sha256:a39c36f4218a5bb668b4f06874d676d35a035ee668e6e7e3538835c703634b84"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
+    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "40b4aee571e46a7a8d292b7e7c7239854913f85c949cce1d00a65428d7654c85"
diff --git a/Chapter08/pyproject.toml b/Chapter08/pyproject.toml
new file mode 100644
index 0000000..f5e68f4
--- /dev/null
+++ b/Chapter08/pyproject.toml
@@ -0,0 +1,46 @@
+[tool.poetry]
+name = "Graph Machine Learning - Chapter 8"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+networkx = "==2.5" 
+scikit-learn = "==0.24.0" 
+gensim = "==3.8.3"
+node2vec = "==0.3.3"
+chardet = "==5.2.0"
+tensorflow = "^2.6.0" 
+tensorflow-io-gcs-filesystem = "==0.21.0" # This needs pinning - see https://stackoverflow.com/a/76477590 
+protobuf= "^3.20"
+python-louvain = "==0.16"
+# communities = "==2.2.0"
+# This is what is holding us back to python 3.8
+stellargraph = "^1.2.1"
+nltk = "==3.5"
+fasttext-wheel = "==0.9.2"
+langdetect = "~1.0"
+spacy = "^3.7.0"
+en-core-web-md = "==3.7.1"
+# Torch
+torch = "^2.1.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+# torch-sparse = {version = "^0.6.18", source = "torch-wheels"}
+torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+torch-scatter = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+pyg-lib = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/pyg_lib-0.4.0%2Bpt21cpu-cp38-cp38-linux_x86_64.whl"}
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Chapter08/requirements.txt b/Chapter08/requirements.txt
new file mode 100644
index 0000000..9f3f251
--- /dev/null
+++ b/Chapter08/requirements.txt
@@ -0,0 +1,164 @@
+absl-py==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+aiohappyeyeballs==2.4.3 ; python_version >= "3.8" and python_version < "3.9"
+aiohttp==3.10.11 ; python_version >= "3.8" and python_version < "3.9"
+aiosignal==1.3.1 ; python_version >= "3.8" and python_version < "3.9"
+annotated-types==0.7.0 ; python_version >= "3.8" and python_version < "3.9"
+appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+astunparse==1.6.3 ; python_version >= "3.8" and python_version < "3.9"
+async-timeout==5.0.1 ; python_version >= "3.8" and python_version < "3.9"
+attrs==24.2.0 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+blis==0.7.11 ; python_version >= "3.8" and python_version < "3.9"
+cachetools==5.5.0 ; python_version >= "3.8" and python_version < "3.9"
+catalogue==2.0.10 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+chardet==5.2.0 ; python_version >= "3.8" and python_version < "3.9"
+charset-normalizer==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+click==8.1.7 ; python_version >= "3.8" and python_version < "3.9"
+cloudpathlib==0.20.0 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+confection==0.1.5 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+cymem==2.0.8 ; python_version >= "3.8" and python_version < "3.9"
+cython==0.29.14 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.8 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+en-core-web-md @ https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.7.1/en_core_web_md-3.7.1-py3-none-any.whl ; python_version >= "3.8" and python_version < "3.9"
+executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+fasttext-wheel==0.9.2 ; python_version >= "3.8" and python_version < "3.9"
+filelock==3.16.1 ; python_version >= "3.8" and python_version < "3.9"
+flatbuffers==2.0.7 ; python_version >= "3.8" and python_version < "3.9"
+frozenlist==1.5.0 ; python_version >= "3.8" and python_version < "3.9"
+fsspec==2024.10.0 ; python_version >= "3.8" and python_version < "3.9"
+gast==0.4.0 ; python_version >= "3.8" and python_version < "3.9"
+gensim==3.8.3 ; python_version >= "3.8" and python_version < "3.9"
+google-auth-oauthlib==1.0.0 ; python_version >= "3.8" and python_version < "3.9"
+google-auth==2.36.0 ; python_version >= "3.8" and python_version < "3.9"
+google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+grpcio==1.68.0 ; python_version >= "3.8" and python_version < "3.9"
+h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.10 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.2 ; python_version >= "3.8" and python_version < "3.9"
+jinja2==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9"
+keras-preprocessing==1.1.2 ; python_version >= "3.8" and python_version < "3.9"
+keras==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9"
+langcodes==3.4.1 ; python_version >= "3.8" and python_version < "3.9"
+langdetect==1.0.9 ; python_version >= "3.8" and python_version < "3.9"
+language-data==1.2.0 ; python_version >= "3.8" and python_version < "3.9"
+libclang==18.1.1 ; python_version >= "3.8" and python_version < "3.9"
+lightning-utilities==0.11.8 ; python_version >= "3.8" and python_version < "3.9"
+marisa-trie==1.2.1 ; python_version >= "3.8" and python_version < "3.9"
+markdown-it-py==3.0.0 ; python_version >= "3.8" and python_version < "3.9"
+markdown==3.7 ; python_version >= "3.8" and python_version < "3.9"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+mdurl==0.1.2 ; python_version >= "3.8" and python_version < "3.9"
+mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9"
+multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+murmurhash==1.0.10 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9"
+networkx==2.5 ; python_version >= "3.8" and python_version < "3.9"
+nltk==3.5 ; python_version >= "3.8" and python_version < "3.9"
+node2vec==0.3.3 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.24.4 ; python_version >= "3.8" and python_version < "3.9"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+packaging==24.2 ; python_version >= "3.8" and python_version < "3.9"
+pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9"
+preshed==3.0.9 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9"
+propcache==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9"
+psutil==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9"
+pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9"
+pybind11==2.13.6 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pydantic-core==2.23.4 ; python_version >= "3.8" and python_version < "3.9"
+pydantic==2.9.2 ; python_version >= "3.8" and python_version < "3.9"
+pyg-lib @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/pyg_lib-0.4.0%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9"
+python-louvain==0.16 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==308 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9"
+regex==2024.11.6 ; python_version >= "3.8" and python_version < "3.9"
+requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9"
+rich==13.9.4 ; python_version >= "3.8" and python_version < "3.9"
+rsa==4.9 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==0.24.0 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+setuptools==75.3.0 ; python_version >= "3.8" and python_version < "3.9"
+shellingham==1.5.4 ; python_version >= "3.8" and python_version < "3.9"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==7.0.5 ; python_version >= "3.8" and python_version < "3.9"
+spacy-legacy==3.0.12 ; python_version >= "3.8" and python_version < "3.9"
+spacy-loggers==1.0.5 ; python_version >= "3.8" and python_version < "3.9"
+spacy==3.7.5 ; python_version >= "3.8" and python_version < "3.9"
+srsly==2.4.8 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9"
+sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9"
+tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9"
+tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9"
+termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9"
+thinc==8.2.5 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9"
+torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9"
+torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp38-cp38-linux_x86_64.whl ; python_version >= "3.8" and python_version < "3.9"
+torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9"
+torchmetrics==1.5.2 ; python_version >= "3.8" and python_version < "3.9"
+torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.67.0 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9"
+typer==0.13.0 ; python_version >= "3.8" and python_version < "3.9"
+typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9"
+tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9"
+wasabi==1.1.3 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9"
+weasel==0.4.1 ; python_version >= "3.8" and python_version < "3.9"
+werkzeug==3.0.6 ; python_version >= "3.8" and python_version < "3.9"
+wheel==0.45.0 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+yarl==1.15.2 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter07/subject_object_extraction.py b/Chapter08/subject_object_extraction.py
similarity index 99%
rename from Chapter07/subject_object_extraction.py
rename to Chapter08/subject_object_extraction.py
index 4ad5c53..0154406 100644
--- a/Chapter07/subject_object_extraction.py
+++ b/Chapter08/subject_object_extraction.py
@@ -143,11 +143,11 @@ def findSVOs(tokens, output="str"):
                 for obj in objs:
                     objNegated = isNegated(obj)
                     
-                    if output is "str":
+                    if output == "str":
                         element = (
                             sub.lower_, "!" + v.lower_ if verbNegated or objNegated else v.lower_, obj.lower_
                         )
-                    elif output is "obj":
+                    elif output == "obj":
                         element = (sub, (v, verbNegated or objNegated), obj)
                     
                     svos.append(element)
diff --git a/Chapter09/01_Credit_card_edges_classification.ipynb b/Chapter09/01_Credit_card_edges_classification.ipynb
new file mode 100644
index 0000000..d02b03e
--- /dev/null
+++ b/Chapter09/01_Credit_card_edges_classification.ipynb
@@ -0,0 +1,1387 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Z12nIL0GmtKF"
+   },
+   "source": [
+    "# Machine learning for Credit Card Transactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "Plnw0bRc_Mks"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "default_edge_color = 'gray'\n",
+    "default_node_color = '#407cc9'\n",
+    "enhanced_node_color = '#f5b042'\n",
+    "enhanced_edge_color = '#cc2f04'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(f\"{os.getcwd()}/..\")\n",
+    "\n",
+    "from utils import DATA_DIR\n",
+    "\n",
+    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Vye1SbKAnI_A"
+   },
+   "source": [
+    "## Load Dataset and build graph"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "os.environ[\"KAGGLE_USERNAME\"]=...\n",
+    "os.environ[\"KAGGLE_KEY\"]=..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import kaggle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
+    "from kaggle.api_client import ApiClient\n",
+    "\n",
+    "api = KaggleApi(ApiClient())\n",
+    "api.authenticate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset URL: https://www.kaggle.com/datasets/kartik2112/fraud-detection\n",
+      "License(s): CC0-1.0\n",
+      "fraud-detection.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
+     ]
+    }
+   ],
+   "source": [
+    "api.dataset_download_cli(\"kartik2112/fraud-detection\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zipfile import ZipFile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myzip = ZipFile(\"fraud-detection.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp_file = myzip.extract(\"fraudTrain.csv\", path=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myzip.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>trans_date_trans_time</th>\n",
+       "      <th>cc_num</th>\n",
+       "      <th>merchant</th>\n",
+       "      <th>category</th>\n",
+       "      <th>amt</th>\n",
+       "      <th>first</th>\n",
+       "      <th>last</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>street</th>\n",
+       "      <th>...</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>city_pop</th>\n",
+       "      <th>job</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>trans_num</th>\n",
+       "      <th>unix_time</th>\n",
+       "      <th>merch_lat</th>\n",
+       "      <th>merch_long</th>\n",
+       "      <th>is_fraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>811778</th>\n",
+       "      <td>811778</td>\n",
+       "      <td>2019-12-07 10:55:06</td>\n",
+       "      <td>676173792455</td>\n",
+       "      <td>fraud_Zieme, Bode and Dooley</td>\n",
+       "      <td>gas_transport</td>\n",
+       "      <td>86.19</td>\n",
+       "      <td>Brittany</td>\n",
+       "      <td>Cox</td>\n",
+       "      <td>F</td>\n",
+       "      <td>07177 William Dale Apt. 547</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.0287</td>\n",
+       "      <td>-118.4924</td>\n",
+       "      <td>92043</td>\n",
+       "      <td>Civil engineer, contracting</td>\n",
+       "      <td>1961-04-25</td>\n",
+       "      <td>f32d1f4b2a918f4c2f6acdc83033ee35</td>\n",
+       "      <td>1354877706</td>\n",
+       "      <td>33.287851</td>\n",
+       "      <td>-118.740970</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1101718</th>\n",
+       "      <td>1101718</td>\n",
+       "      <td>2020-04-03 13:10:06</td>\n",
+       "      <td>30518206766474</td>\n",
+       "      <td>fraud_Lind-Buckridge</td>\n",
+       "      <td>entertainment</td>\n",
+       "      <td>85.81</td>\n",
+       "      <td>Tamara</td>\n",
+       "      <td>Martinez</td>\n",
+       "      <td>F</td>\n",
+       "      <td>471 Marquez Prairie Suite 680</td>\n",
+       "      <td>...</td>\n",
+       "      <td>36.7154</td>\n",
+       "      <td>-89.6287</td>\n",
+       "      <td>1019</td>\n",
+       "      <td>Aeronautical engineer</td>\n",
+       "      <td>1979-01-26</td>\n",
+       "      <td>f5dad8e2d7c39d81502d846a20286659</td>\n",
+       "      <td>1364994606</td>\n",
+       "      <td>36.539950</td>\n",
+       "      <td>-89.857416</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800013</th>\n",
+       "      <td>800013</td>\n",
+       "      <td>2019-12-04 07:07:04</td>\n",
+       "      <td>4658490815480264</td>\n",
+       "      <td>fraud_Hackett-Lueilwitz</td>\n",
+       "      <td>grocery_pos</td>\n",
+       "      <td>99.30</td>\n",
+       "      <td>Tara</td>\n",
+       "      <td>Richards</td>\n",
+       "      <td>F</td>\n",
+       "      <td>4879 Cristina Station</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.9636</td>\n",
+       "      <td>-79.7853</td>\n",
+       "      <td>184</td>\n",
+       "      <td>Systems developer</td>\n",
+       "      <td>1945-11-04</td>\n",
+       "      <td>1d023bc78ab93ab65a35bbb53bcc67bd</td>\n",
+       "      <td>1354604824</td>\n",
+       "      <td>39.582872</td>\n",
+       "      <td>-78.838550</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398945</th>\n",
+       "      <td>398945</td>\n",
+       "      <td>2019-06-30 18:43:08</td>\n",
+       "      <td>4716561796955522</td>\n",
+       "      <td>fraud_Lynch-Wisozk</td>\n",
+       "      <td>home</td>\n",
+       "      <td>42.09</td>\n",
+       "      <td>Lauren</td>\n",
+       "      <td>Anderson</td>\n",
+       "      <td>F</td>\n",
+       "      <td>11014 Chad Lake Apt. 573</td>\n",
+       "      <td>...</td>\n",
+       "      <td>48.2777</td>\n",
+       "      <td>-112.8456</td>\n",
+       "      <td>743</td>\n",
+       "      <td>Water engineer</td>\n",
+       "      <td>1972-05-04</td>\n",
+       "      <td>dbf6c06d3277438afdf7af883fb4285f</td>\n",
+       "      <td>1341081788</td>\n",
+       "      <td>48.310513</td>\n",
+       "      <td>-112.837535</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207455</th>\n",
+       "      <td>207455</td>\n",
+       "      <td>2019-04-15 19:57:49</td>\n",
+       "      <td>3528407217576457</td>\n",
+       "      <td>fraud_Fisher-Schowalter</td>\n",
+       "      <td>shopping_net</td>\n",
+       "      <td>4.24</td>\n",
+       "      <td>Patricia</td>\n",
+       "      <td>Leach</td>\n",
+       "      <td>F</td>\n",
+       "      <td>71309 Martinez Stravenue</td>\n",
+       "      <td>...</td>\n",
+       "      <td>36.4715</td>\n",
+       "      <td>-82.4834</td>\n",
+       "      <td>87124</td>\n",
+       "      <td>Warden/ranger</td>\n",
+       "      <td>1987-02-14</td>\n",
+       "      <td>88814660aba0101b174e1e8137f4a7af</td>\n",
+       "      <td>1334519869</td>\n",
+       "      <td>37.329094</td>\n",
+       "      <td>-82.070746</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
+       "811778       811778   2019-12-07 10:55:06      676173792455   \n",
+       "1101718     1101718   2020-04-03 13:10:06    30518206766474   \n",
+       "800013       800013   2019-12-04 07:07:04  4658490815480264   \n",
+       "398945       398945   2019-06-30 18:43:08  4716561796955522   \n",
+       "207455       207455   2019-04-15 19:57:49  3528407217576457   \n",
+       "\n",
+       "                             merchant       category    amt     first  \\\n",
+       "811778   fraud_Zieme, Bode and Dooley  gas_transport  86.19  Brittany   \n",
+       "1101718          fraud_Lind-Buckridge  entertainment  85.81    Tamara   \n",
+       "800013        fraud_Hackett-Lueilwitz    grocery_pos  99.30      Tara   \n",
+       "398945             fraud_Lynch-Wisozk           home  42.09    Lauren   \n",
+       "207455        fraud_Fisher-Schowalter   shopping_net   4.24  Patricia   \n",
+       "\n",
+       "             last gender                         street  ...      lat  \\\n",
+       "811778        Cox      F    07177 William Dale Apt. 547  ...  34.0287   \n",
+       "1101718  Martinez      F  471 Marquez Prairie Suite 680  ...  36.7154   \n",
+       "800013   Richards      F          4879 Cristina Station  ...  39.9636   \n",
+       "398945   Anderson      F       11014 Chad Lake Apt. 573  ...  48.2777   \n",
+       "207455      Leach      F       71309 Martinez Stravenue  ...  36.4715   \n",
+       "\n",
+       "             long  city_pop                          job         dob  \\\n",
+       "811778  -118.4924     92043  Civil engineer, contracting  1961-04-25   \n",
+       "1101718  -89.6287      1019        Aeronautical engineer  1979-01-26   \n",
+       "800013   -79.7853       184            Systems developer  1945-11-04   \n",
+       "398945  -112.8456       743               Water engineer  1972-05-04   \n",
+       "207455   -82.4834     87124                Warden/ranger  1987-02-14   \n",
+       "\n",
+       "                                trans_num   unix_time  merch_lat  merch_long  \\\n",
+       "811778   f32d1f4b2a918f4c2f6acdc83033ee35  1354877706  33.287851 -118.740970   \n",
+       "1101718  f5dad8e2d7c39d81502d846a20286659  1364994606  36.539950  -89.857416   \n",
+       "800013   1d023bc78ab93ab65a35bbb53bcc67bd  1354604824  39.582872  -78.838550   \n",
+       "398945   dbf6c06d3277438afdf7af883fb4285f  1341081788  48.310513 -112.837535   \n",
+       "207455   88814660aba0101b174e1e8137f4a7af  1334519869  37.329094  -82.070746   \n",
+       "\n",
+       "         is_fraud  \n",
+       "811778          0  \n",
+       "1101718         0  \n",
+       "800013          0  \n",
+       "398945          0  \n",
+       "207455          0  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(os.path.join(DATA_DIR, \"fraudTrain.csv\"))\n",
+    "df = df[df[\"is_fraud\"]==0].sample(frac=0.20, random_state=42).append(df[df[\"is_fraud\"] == 1])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    257834\n",
+       "1      7506\n",
+       "Name: is_fraud, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"is_fraud\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_graph_bipartite(df_input, graph_type=nx.Graph()):\n",
+    "    df = df_input.copy()\n",
+    "    mapping = {x:node_id for node_id,x in enumerate(set(df[\"cc_num\"].values.tolist() + df[\"merchant\"].values.tolist()))}\n",
+    "    df[\"from\"] = df[\"cc_num\"].apply(lambda x: mapping[x])\n",
+    "    df[\"to\"] = df[\"merchant\"].apply(lambda x: mapping[x])\n",
+    "    df = df[['from', 'to', \"amt\", \"is_fraud\"]].groupby(['from', 'to']).agg({\"is_fraud\": \"sum\", \"amt\": \"sum\"}).reset_index()\n",
+    "    df[\"is_fraud\"] = df[\"is_fraud\"].apply(lambda x: 1 if x>0 else 0)\n",
+    "\n",
+    "    name = graph_type.name\n",
+    "    \n",
+    "    G = nx.from_edgelist(df[[\"from\", \"to\"]].values, create_using=graph_type)\n",
+    "\n",
+    "    G.name = name\n",
+    "    \n",
+    "    nx.set_node_attributes(G,{x:1 for x in df[\"from\"].unique()}, \"bipartite\")\n",
+    "    nx.set_node_attributes(G,{x:2 for x in df[\"to\"].unique()}, \"bipartite\")\n",
+    "    \n",
+    "    nx.set_edge_attributes(G, \n",
+    "                       {(int(x[\"from\"]), int(x[\"to\"])):x[\"is_fraud\"] for idx, x in df[[\"from\",\"to\",\"is_fraud\"]].iterrows()}, \n",
+    "                       \"label\")\n",
+    "\n",
+    "    nx.set_edge_attributes(G, \n",
+    "                       {(int(x[\"from\"]), int(x[\"to\"])):x[\"amt\"] for idx, x in df[[\"from\",\"to\",\"amt\"]].iterrows()}, \n",
+    "                       \"weight\")\n",
+    "    return G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_graph_tripartite(df_input, graph_type=nx.Graph()):\n",
+    "    df = df_input.copy()\n",
+    "    mapping = {x:node_id for node_id,x in enumerate(set(df.index.values.tolist() + \n",
+    "                                                        df[\"cc_num\"].values.tolist() + \n",
+    "                                                        df[\"merchant\"].values.tolist()))}\n",
+    "    df[\"in_node\"] = df[\"cc_num\"].apply(lambda x: mapping[x])\n",
+    "    df[\"out_node\"] = df[\"merchant\"].apply(lambda x: mapping[x])\n",
+    "\n",
+    "    name = graph_type.name\n",
+    "    \n",
+    "    G = nx.from_edgelist([(x[\"in_node\"], mapping[idx]) for idx, x in df.iterrows()] +\n",
+    "                         [(x[\"out_node\"], mapping[idx]) for idx, x in df.iterrows()], \n",
+    "                         create_using=graph_type)\n",
+    "\n",
+    "    G.name = name\n",
+    "\n",
+    "    nx.set_node_attributes(G,{x[\"in_node\"]:1 for idx,x in df.iterrows()}, \"bipartite\")\n",
+    "    nx.set_node_attributes(G,{x[\"out_node\"]:2 for idx,x in df.iterrows()}, \"bipartite\")\n",
+    "    nx.set_node_attributes(G,{mapping[idx]:3 for idx, x in df.iterrows()}, \"bipartite\")\n",
+    "\n",
+    "    nx.set_edge_attributes(G,{(x[\"in_node\"], mapping[idx]):x[\"is_fraud\"] for idx, x in df.iterrows()}, \"label\")\n",
+    "    nx.set_edge_attributes(G,{(x[\"out_node\"], mapping[idx]):x[\"is_fraud\"] for idx, x in df.iterrows()}, \"label\")\n",
+    "\n",
+    "    nx.set_edge_attributes(G,{(x[\"in_node\"], mapping[idx]):x[\"amt\"] for idx, x in df.iterrows()}, \"weight\")\n",
+    "    nx.set_edge_attributes(G,{(x[\"out_node\"], mapping[idx]):x[\"amt\"] for idx, x in df.iterrows()}, \"weight\")\n",
+    "    return G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_bu = build_graph_bipartite(df, nx.Graph(name=\"Bipartite Undirect\"))\n",
+    "G_bd = build_graph_bipartite(df, nx.DiGraph(name=\"Bipartite Direct\"))\n",
+    "\n",
+    "G_tu = build_graph_tripartite(df, nx.Graph(name=\"Tripartite Undirect\"))\n",
+    "G_td = build_graph_tripartite(df, nx.DiGraph(name=\"Tripartite Direct\")) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from networkx.algorithms import bipartite\n",
+    "all([bipartite.is_bipartite(G) for G in [G_bu,G_tu]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2z2PCthzneat"
+   },
+   "source": [
+    "## Network Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: Bipartite Undirect\n",
+      "Type: Graph\n",
+      "Number of nodes: 1676\n",
+      "Number of edges: 201725\n",
+      "Average degree: 240.7220\n",
+      "Name: Tripartite Undirect\n",
+      "Type: Graph\n",
+      "Number of nodes: 267016\n",
+      "Number of edges: 530680\n",
+      "Average degree:   3.9749\n"
+     ]
+    }
+   ],
+   "source": [
+    "for G in [G_bu, G_tu]:\n",
+    "    print(nx.info(G))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    plt.subplot(1,2,ith+1)\n",
+    "    degrees = pd.Series({k: v for k, v in nx.degree(G)})\n",
+    "    degrees.plot.hist()\n",
+    "    plt.yscale(\"log\")\n",
+    "    plt.xlabel(\"Degree\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5.03 58.25 98.44 215.66 15305.95]\n",
+      "[4.21 48.51 76.40 147.10 15305.95]\n"
+     ]
+    }
+   ],
+   "source": [
+    "quant_dist = {}\n",
+    "\n",
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    allEdgesWeights = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in G.edges(data=True)})\n",
+    "\n",
+    "    quant_dist[G.name] = np.quantile(allEdgesWeights.values,[0.10,0.50,0.70,0.9,1.0]) \n",
+    "    print(quant_dist[G.name])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "\n",
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    plt.subplot(1,2,ith+1)\n",
+    "    allEdgesWeightsFiltered = pd.Series({(d[0], d[1]): d[2][\"weight\"] for d in G.edges(data=True) \n",
+    "                                         if d[2][\"weight\"] < quant_dist[G.name][-2]})\n",
+    "    allEdgesWeightsFiltered.plot.hist(bins=40)\n",
+    "    plt.yscale(\"log\")\n",
+    "    plt.xlabel(\"Edge Weight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Betweeness centrality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bipartite Undirect: 0.0007220813495247776\n",
+      "Tripartite Undirect: 1.3751972752978146e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "\n",
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    plt.subplot(1,2,ith+1)\n",
+    "\n",
+    "    bC = nx.betweenness_centrality(G, k=200)\n",
+    "    bc_distr = pd.Series(bC)\n",
+    "    print(f\"{G.name}: {bc_distr.mean()}\")\n",
+    "    bc_distr.plot.hist()\n",
+    "    plt.yscale(\"log\")\n",
+    "    plt.xlabel(\"Betweeness centrality\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Degree centrality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "94viGU4vserg",
+    "outputId": "ea65df57-df57-4e51-f396-9c808f274766"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,4))\n",
+    "\n",
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    plt.subplot(1,2,ith+1)\n",
+    "    \n",
+    "    deg_C = nx.degree_centrality(G)\n",
+    "    degc_distr = pd.Series(deg_C) #.apply(np.log)\n",
+    "    degc_distr.plot.hist()\n",
+    "\n",
+    "    plt.xlabel(\"Degree centrality\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Assortativity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MQOah_yDtbaW",
+    "outputId": "558fc1ea-f457-4386-b5ce-8dc61f8f0115"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bipartite Undirect: -0.13774320410491867\n",
+      "Tripartite Undirect: -0.8079472914876619\n"
+     ]
+    }
+   ],
+   "source": [
+    "for ith, G in enumerate([G_bu, G_tu]):\n",
+    "    assortativity = nx.degree_pearson_correlation_coefficient(G)\n",
+    "    print(f\"{G.name}: {assortativity}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c8peWeN9nh1m"
+   },
+   "source": [
+    "### Community Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Bipartite Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G=G_bu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parts = community.best_partition(G, random_state=42, weight='weight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2     465\n",
+       "13    234\n",
+       "0     148\n",
+       "5     133\n",
+       "10    113\n",
+       "9      99\n",
+       "8      93\n",
+       "1      87\n",
+       "12     80\n",
+       "6      72\n",
+       "4      66\n",
+       "7      57\n",
+       "11     20\n",
+       "3       9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities = pd.Series(parts)\n",
+    "communities.value_counts().sort_values(ascending=False).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Community size')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "communities.value_counts().plot.hist(bins=20)\n",
+    "plt.xlabel(\"Community size\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4     20.289855\n",
+       "5     18.671454\n",
+       "11    18.181818\n",
+       "8     17.281879\n",
+       "6     15.591398\n",
+       "0     14.538462\n",
+       "7     10.769231\n",
+       "12    10.758377\n",
+       "1      9.883721\n",
+       "10     9.622642\n",
+       "9      7.707317\n",
+       "2      2.969694\n",
+       "13     1.297648\n",
+       "3      0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graphs = []\n",
+    "d = {}\n",
+    "for x in communities.unique():\n",
+    "    tmp = nx.subgraph(G, communities[communities==x].index)\n",
+    "    fraud_edges = sum(nx.get_edge_attributes(tmp, \"label\").values())\n",
+    "    ratio = 0 if fraud_edges == 0 else (fraud_edges/tmp.number_of_edges())*100\n",
+    "    d[x] = ratio\n",
+    "    graphs += [tmp]\n",
+    "\n",
+    "pd.Series(d).sort_values(ascending=False).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Fraud over genuine ratio')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.Series(d).plot.hist(bins=20) \n",
+    "plt.xlabel(\"Fraud over genuine ratio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gId = 8\n",
+    "plt.figure(figsize=(10,10))\n",
+    "spring_pos = nx.spring_layout(graphs[gId])\n",
+    "plt.axis(\"off\")\n",
+    "edge_colors = [\"r\" if x == 1 else \"g\" for x in nx.get_edge_attributes(graphs[gId], 'label').values()]\n",
+    "nx.draw_networkx(graphs[gId], pos=spring_pos, node_color=default_node_color, \n",
+    "                 edge_color=edge_colors, with_labels=False, node_size=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tripartite Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = G_tu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parts = community.best_partition(G, random_state=42, weight='weight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11    4828\n",
+       "99    4493\n",
+       "26    4313\n",
+       "94    4115\n",
+       "8     4036\n",
+       "5     4011\n",
+       "82    3768\n",
+       "97    3740\n",
+       "2     3712\n",
+       "83    3699\n",
+       "64    3600\n",
+       "7     3598\n",
+       "53    3522\n",
+       "48    3457\n",
+       "73    3341\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "communities = pd.Series(parts)\n",
+    "communities.value_counts().sort_values(ascending=False).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Community size')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "communities.value_counts().plot.hist(bins=20)\n",
+    "plt.xlabel(\"Community size\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6     6.857728\n",
+       "94    6.551151\n",
+       "8     5.966981\n",
+       "1     5.870918\n",
+       "89    5.760271\n",
+       "14    5.653863\n",
+       "76    5.628272\n",
+       "57    5.205479\n",
+       "80    5.182421\n",
+       "98    5.100182\n",
+       "30    5.078895\n",
+       "40    5.047319\n",
+       "34    5.023761\n",
+       "86    4.874715\n",
+       "96    4.678899\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graphs = []\n",
+    "d = {}\n",
+    "for x in communities.unique():\n",
+    "    tmp = nx.subgraph(G, communities[communities==x].index)\n",
+    "    fraud_edges = sum(nx.get_edge_attributes(tmp, \"label\").values())\n",
+    "    ratio = 0 if fraud_edges == 0 else (fraud_edges/tmp.number_of_edges())*100\n",
+    "    d[x] = ratio\n",
+    "    graphs += [tmp]\n",
+    "\n",
+    "pd.Series(d).sort_values(ascending=False).head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Fraud over genuine ratio')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.Series(d).plot.hist(bins=20) \n",
+    "plt.xlabel(\"Fraud over genuine ratio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gId = 8\n",
+    "plt.figure(figsize=(10,10))\n",
+    "spring_pos = nx.spring_layout(graphs[gId])\n",
+    "plt.axis(\"off\")\n",
+    "edge_colors = [\"r\" if x == 1 else \"g\" for x in nx.get_edge_attributes(graphs[gId], 'label').values()]\n",
+    "nx.draw_networkx(graphs[gId], pos=spring_pos, node_color=default_node_color, \n",
+    "                 edge_color=edge_colors, with_labels=False, node_size=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Supervised Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1    7506\n",
+      "0    7506\n",
+      "Name: is_fraud, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.utils import resample\n",
+    "\n",
+    "df_majority = df[df.is_fraud==0]\n",
+    "df_minority = df[df.is_fraud==1]\n",
+    "\n",
+    "df_maj_dowsampled = resample(df_majority,\n",
+    "                             n_samples=len(df_minority),\n",
+    "                             random_state=42)\n",
+    "\n",
+    "df_downsampled = pd.concat([df_minority, df_maj_dowsampled])\n",
+    "\n",
+    "print(df_downsampled.is_fraud.value_counts())\n",
+    "G_down = build_graph_bipartite(df_downsampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n",
+    "train_edges, test_edges, train_labels, test_labels = train_test_split(list(range(len(G_down.edges))), \n",
+    "                                                                      list(nx.get_edge_attributes(G_down, \"label\").values()), \n",
+    "                                                                      test_size=0.20, \n",
+    "                                                                      random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edgs = list(G_down.edges)\n",
+    "train_graph = G_down.edge_subgraph([edgs[x] for x in train_edges]).copy()\n",
+    "train_graph.add_nodes_from(list(set(G_down.nodes) - set(train_graph.nodes)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|██████████████████████████| 1672/1672 [00:01<00:00, 1074.22it/s]\n",
+      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████| 10/10 [00:34<00:00,  3.43s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from node2vec import Node2Vec\n",
+    "from node2vec.edges import HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder\n",
+    "\n",
+    "node2vec_train = Node2Vec(train_graph, weight_key='weight')\n",
+    "model_train = node2vec_train.fit(window=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'node2vec.edges.HadamardEmbedder'>\n",
+      "Precision: 0.7599611273080661\n",
+      "Recall: 0.5308893414799728\n",
+      "F1-Score: 0.6250999200639488\n",
+      "<class 'node2vec.edges.AverageEmbedder'>\n",
+      "Precision: 0.7164073550212164\n",
+      "Recall: 0.6877121520706042\n",
+      "F1-Score: 0.7017665396605473\n",
+      "<class 'node2vec.edges.WeightedL1Embedder'>\n",
+      "Precision: 0.6199316072300928\n",
+      "Recall: 0.8615071283095723\n",
+      "F1-Score: 0.7210227272727273\n",
+      "<class 'node2vec.edges.WeightedL2Embedder'>\n",
+      "Precision: 0.6205378973105135\n",
+      "Recall: 0.8615071283095723\n",
+      "F1-Score: 0.7214326321773735\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier \n",
+    "from sklearn import metrics \n",
+    "\n",
+    "classes = [HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder]\n",
+    "for cl in classes:\n",
+    "    embeddings_train = cl(keyed_vectors=model_train.wv) \n",
+    "\n",
+    "    train_embeddings = [embeddings_train[str(edgs[x][0]), str(edgs[x][1])] for x in train_edges]\n",
+    "    test_embeddings = [embeddings_train[str(edgs[x][0]), str(edgs[x][1])] for x in test_edges]\n",
+    "    \n",
+    "    rf = RandomForestClassifier(n_estimators=1000, random_state=42) \n",
+    "    rf.fit(train_embeddings, train_labels); \n",
+    "\n",
+    "    y_pred = rf.predict(test_embeddings)\n",
+    "    print(cl)\n",
+    "    print('Precision:', metrics.precision_score(test_labels, y_pred)) \n",
+    "    print('Recall:', metrics.recall_score(test_labels, y_pred)) \n",
+    "    print('F1-Score:', metrics.f1_score(test_labels, y_pred)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R4Vk5GnxcWF2"
+   },
+   "source": [
+    "## Unupervised Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing transition probabilities: 100%|███████████████████████████| 1672/1672 [00:02<00:00, 648.89it/s]\n",
+      "Generating walks (CPU: 1): 100%|█████████████████████████████████████████| 10/10 [00:37<00:00,  3.73s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nod2vec_unsup = Node2Vec(G_down, weight_key='weight')\n",
+    "unsup_vals = nod2vec_unsup.fit(window=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'node2vec.edges.HadamardEmbedder'>\n",
+      "NMI: 0.35357823566898267\n",
+      "Homogeneity: 0.34218086499728617\n",
+      "Completeness: 0.3658315862407637\n",
+      "V-Measure: 0.3536112067077341\n",
+      "<class 'node2vec.edges.AverageEmbedder'>\n",
+      "NMI: 0.06722811041159231\n",
+      "Homogeneity: 0.06631911824164934\n",
+      "Completeness: 0.06825845272397836\n",
+      "V-Measure: 0.06727481206136121\n",
+      "<class 'node2vec.edges.WeightedL1Embedder'>\n",
+      "NMI: 0.06433043718212811\n",
+      "Homogeneity: 0.06437090274809783\n",
+      "Completeness: 0.0643823427937208\n",
+      "V-Measure: 0.06437662226267107\n",
+      "<class 'node2vec.edges.WeightedL2Embedder'>\n",
+      "NMI: 0.05071791824826933\n",
+      "Homogeneity: 0.05017348061331937\n",
+      "Completeness: 0.051371296699734594\n",
+      "V-Measure: 0.05076532397326684\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "classes = [HadamardEmbedder, AverageEmbedder, WeightedL1Embedder, WeightedL2Embedder]\n",
+    "true_labels = [x for x in nx.get_edge_attributes(G_down, \"label\").values()]\n",
+    "\n",
+    "for cl in classes:\n",
+    "    embedding_edge = cl(keyed_vectors=unsup_vals.wv) \n",
+    "\n",
+    "    embedding = [embedding_edge[str(x[0]), str(x[1])] for x in G_down.edges()]\n",
+    "    kmeans = KMeans(2, random_state=42).fit(embedding)\n",
+    "    \n",
+    "    \n",
+    "    nmi = metrics.adjusted_mutual_info_score(true_labels, kmeans.labels_)\n",
+    "    ho = metrics.homogeneity_score(true_labels, kmeans.labels_)\n",
+    "    co = metrics.completeness_score(true_labels, kmeans.labels_)\n",
+    "    vmeasure = metrics.v_measure_score(true_labels, kmeans.labels_)\n",
+    "    \n",
+    "    print(cl)\n",
+    "    print('NMI:', nmi)\n",
+    "    print('Homogeneity:', ho)\n",
+    "    print('Completeness:', co)\n",
+    "    print('V-Measure:', vmeasure)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "socialNetwork.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "chap9",
+   "language": "python",
+   "name": "chap9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter09/01_Neo4j_bindings.ipynb b/Chapter09/01_Neo4j_bindings.ipynb
deleted file mode 100644
index 9d52408..0000000
--- a/Chapter09/01_Neo4j_bindings.ipynb
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Graph Database Connection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following, we will show you how to connect and query data on Neo4j, using python. \n",
-    "\n",
-    "**IMPORTANT NOTE**\n",
-    "\n",
-    "This notebook requires that you have access to a working version of Neo4j. In order to install Neo4j locally, we advise you to refer to the Neo4j webpage (https://neo4j.com/download/) or to use docker (https://hub.docker.com/_/neo4j)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"./dataset/movieCreationQuery.txt\", \"rb\") as fid:\n",
-    "    lines = fid.readlines()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \" \".join([line.decode(\"utf-8\").replace(\"\\n\", \"\") for line in lines])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from neo4j import GraphDatabase"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uri = \"neo4j://localhost:7687\"\n",
-    "driver = GraphDatabase.driver(uri, auth=(\"neo4j\", \"neo5j\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_query(tx, query):\n",
-    "    return list(tx.run(query))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with driver.session() as session:\n",
-    "    session.write_transaction(run_query, query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"MATCH (n) RETURN count(*)\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<Record count(*)=342>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with driver.session() as session:\n",
-    "    result = session.read_transaction(run_query, query)\n",
-    "[r for r in result]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Delete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with driver.session() as session:\n",
-    "    result = session.write_transaction(run_query, \"MATCH (n)-[e]-() DELETE n, e\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ml-book-9",
-   "language": "python",
-   "name": "ml-book-9"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Chapter09/poetry.lock b/Chapter09/poetry.lock
new file mode 100644
index 0000000..2544f23
--- /dev/null
+++ b/Chapter09/poetry.lock
@@ -0,0 +1,1726 @@
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+
+[[package]]
+name = "zipp"
+version = "3.20.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
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+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.8"
+content-hash = "20fc82932f9d03242bc017adff9e56d3e54015c0e540ecfcfb3c350ed2371e61"
diff --git a/Chapter09/pyproject.toml b/Chapter09/pyproject.toml
new file mode 100644
index 0000000..683bc65
--- /dev/null
+++ b/Chapter09/pyproject.toml
@@ -0,0 +1,26 @@
+[tool.poetry]
+name = "Graph Machine Learning - Chapter 9"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.8"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+networkx = "==2.5" 
+scikit-learn = "==0.24.0" 
+pandas= "==1.1.3"
+gensim = "==3.8.3"
+node2vec = "==0.3.3"
+python-louvain = "==0.16"
+kaggle = "*"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/Chapter09/requirements.txt b/Chapter09/requirements.txt
new file mode 100644
index 0000000..93304bb
--- /dev/null
+++ b/Chapter09/requirements.txt
@@ -0,0 +1,67 @@
+appnope==0.1.4 ; python_version >= "3.8" and python_version < "3.9" and (platform_system == "Darwin" or sys_platform == "darwin")
+asttokens==2.4.1 ; python_version >= "3.8" and python_version < "3.9"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9"
+bleach==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+certifi==2024.8.30 ; python_version >= "3.8" and python_version < "3.9"
+cffi==1.17.1 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+charset-normalizer==3.4.0 ; python_version >= "3.8" and python_version < "3.9"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.9" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version >= "3.8" and python_version < "3.9"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "3.9"
+cython==0.29.14 ; python_version >= "3.8" and python_version < "3.9"
+debugpy==1.8.9 ; python_version >= "3.8" and python_version < "3.9"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.9"
+executing==2.1.0 ; python_version >= "3.8" and python_version < "3.9"
+gensim==3.8.3 ; python_version >= "3.8" and python_version < "3.9"
+idna==3.10 ; python_version >= "3.8" and python_version < "3.9"
+importlib-metadata==8.5.0 ; python_version >= "3.8" and python_version < "3.9"
+ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9"
+ipython==8.12.3 ; python_version >= "3.8" and python_version < "3.9"
+jedi==0.19.2 ; python_version >= "3.8" and python_version < "3.9"
+joblib==1.4.2 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-client==8.6.3 ; python_version >= "3.8" and python_version < "3.9"
+jupyter-core==5.7.2 ; python_version >= "3.8" and python_version < "3.9"
+kaggle==1.6.17 ; python_version >= "3.8" and python_version < "3.9"
+kiwisolver==1.4.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib-inline==0.1.7 ; python_version >= "3.8" and python_version < "3.9"
+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9"
+nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9"
+networkx==2.5 ; python_version >= "3.8" and python_version < "3.9"
+node2vec==0.3.3 ; python_version >= "3.8" and python_version < "3.9"
+numpy==1.24.4 ; python_version >= "3.8" and python_version < "3.9"
+packaging==24.2 ; python_version >= "3.8" and python_version < "3.9"
+pandas==1.1.3 ; python_version >= "3.8" and python_version < "3.9"
+parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9"
+pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9"
+prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9"
+psutil==6.1.0 ; python_version >= "3.8" and python_version < "3.9"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32"
+pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9"
+pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy"
+pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9"
+pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9"
+python-louvain==0.16 ; python_version >= "3.8" and python_version < "3.9"
+python-slugify==8.0.4 ; python_version >= "3.8" and python_version < "3.9"
+pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9"
+pywin32==308 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9"
+pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9"
+requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9"
+scikit-learn==0.24.0 ; python_version >= "3.8" and python_version < "3.9"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.9"
+smart-open==7.0.5 ; python_version >= "3.8" and python_version < "3.9"
+stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9"
+text-unidecode==1.3 ; python_version >= "3.8" and python_version < "3.9"
+threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9"
+tornado==6.4.2 ; python_version >= "3.8" and python_version < "3.9"
+tqdm==4.67.1 ; python_version >= "3.8" and python_version < "3.9"
+traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9"
+typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9"
+urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9"
+wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9"
+webencodings==0.5.1 ; python_version >= "3.8" and python_version < "3.9"
+wrapt==1.17.0 ; python_version >= "3.8" and python_version < "3.9"
+zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9"
diff --git a/Chapter10/00_Data_Conversion.ipynb b/Chapter10/00_Data_Conversion.ipynb
new file mode 100644
index 0000000..b1db067
--- /dev/null
+++ b/Chapter10/00_Data_Conversion.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "750472d1-ddd0-4a69-914c-77ac2ee9025c",
+   "metadata": {},
+   "source": [
+    "# Data conversion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "551da007-54e2-43b4-aad4-1ee33330510e",
+   "metadata": {},
+   "source": [
+    "This script allows the data conversion of the Cypher query to import the Movie dataset into Neo4j into two dataframes (nodes, and edges) that are imported in JanusGraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2afbf7aa-8903-4f0a-b622-2ed8ff947dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"movieCreationQuery.txt\", \"r\") as fid:\n",
+    "    lines = fid.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b7e2b1d6-260b-4c51-aa50-8ce7573b7563",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "is_edge_regex = re.compile(\"\\[.*\\]\")\n",
+    "\n",
+    "def is_edge(line):\n",
+    "    if is_edge_regex.search(line):\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "is_valid_regex = re.compile(\"\\(.*\\)\")\n",
+    "\n",
+    "def is_valid(line):\n",
+    "    if is_valid_regex.search(line):\n",
+    "        return True\n",
+    "    return False    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e2eb2bcc-ca2a-4204-95a0-55073ac6148d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json \n",
+    "\n",
+    "def dict_serializer(input_dict):\n",
+    "    return {\n",
+    "        k: json.dumps(v) if isinstance(v, dict) or isinstance(v, list) else v\n",
+    "        for k, v in input_dict.items()\n",
+    "    }\n",
+    "\n",
+    "single_quote_parser = lambda props: dict_serializer(json.loads(re.sub(\"(\\w+):\", r\"'\\1':\",  props).replace(\"'\",\"\\\"\")))\n",
+    "double_quote_parser = lambda props: dict_serializer(json.loads(re.sub(\"(\\w+):\", r'\"\\1\":',  props)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "12620971-08dd-4eb2-98f9-a351e44e412d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataclasses import dataclass\n",
+    "\n",
+    "@dataclass\n",
+    "class Node:\n",
+    "    id: str\n",
+    "    label: str\n",
+    "    props: dict\n",
+    "\n",
+    "@dataclass\n",
+    "class Edge:\n",
+    "    id: tuple[str, str]\n",
+    "    label: str\n",
+    "    props: dict\n",
+    "\n",
+    "node_regex = re.compile(r'.*\\((\\w*):(\\w*).*({.*})\\).*')\n",
+    "\n",
+    "def parse_node_line(line):\n",
+    "\n",
+    "    name, label, props = node_regex.match(line).groups()\n",
+    "\n",
+    "    parser = double_quote_parser if '\"' in props else single_quote_parser\n",
+    "\n",
+    "    return Node(name, label, parser(props))\n",
+    "\n",
+    "edge_regex = re.compile(r'^[a-zA-Z\\s]*\\((\\w+)\\)-\\[:(\\w+) ({.*})\\]->\\((\\w+)\\).*')\n",
+    "edge_regex_noprop = re.compile(r'^[a-zA-Z\\s]*\\((\\w+)\\)-\\[:(\\w+)]->\\((\\w+)\\).*')\n",
+    "\n",
+    "def parse_edge_line(line):\n",
+    "\n",
+    "    try:\n",
+    "        source, rel_type, props, target = edge_regex.match(line).groups()\n",
+    "    except:\n",
+    "        source, rel_type, target = edge_regex_noprop.match(line).groups()\n",
+    "        props=\"{}\"\n",
+    "\n",
+    "    parser = double_quote_parser if '\"' in props else single_quote_parser\n",
+    "\n",
+    "    return Edge((source, target), rel_type, parser(props))\n",
+    "\n",
+    "\n",
+    "def parse_line(line: str):\n",
+    "    if not is_valid(line):\n",
+    "        return None\n",
+    "    \n",
+    "    if is_edge(line):\n",
+    "        return parse_edge_line(line)\n",
+    "    return parse_node_line(line)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bedf909d-1132-4be6-a4fc-ce8897e3bbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "line=lines[18]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2d0b79ff-d34b-43db-9e3f-ba7dcbabd85f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Edge(id=('Emil', 'TheMatrix'), label='ACTED_IN', props={'roles': '[\"Emil\"]'})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parse_line(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f7b03500-8d09-4d41-9b3c-f07f91ce8f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parsed_outout = [parse_line(line) for line in lines]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "147efe7d-6044-4711-be5c-caf4c5d65bda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = [item for item in parsed_outout if isinstance(item, Node)]\n",
+    "edges = [item for item in parsed_outout if isinstance(item, Edge)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6bd3f299-569d-4661-a085-0b9a03dd9ede",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "171"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e6190a56-e7f5-4682-b773-ca8fdb3fa46d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "254"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "58b8e97a-a774-4d77-a261-aeea4a112bb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "368f624d-1f3a-42a8-b747-80d39ec71cae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Node(id='TheMatrix', label='Movie', props={'title': 'The Matrix', 'released': 1999, 'tagline': 'Welcome to the Real World'}),\n",
+       " Node(id='Keanu', label='Person', props={'name': 'Keanu Reeves', 'born': 1964}),\n",
+       " Node(id='Carrie', label='Person', props={'name': 'Carrie-Anne Moss', 'born': 1967}),\n",
+       " Node(id='Laurence', label='Person', props={'name': 'Laurence Fishburne', 'born': 1961}),\n",
+       " Node(id='Hugo', label='Person', props={'name': 'Hugo Weaving', 'born': 1960}),\n",
+       " Node(id='LillyW', label='Person', props={'name': 'Lilly Wachowski', 'born': 1967}),\n",
+       " Node(id='LanaW', label='Person', props={'name': 'Lana Wachowski', 'born': 1965}),\n",
+       " Node(id='JoelS', label='Person', props={'name': 'Joel Silver', 'born': 1952}),\n",
+       " Node(id='Emil', label='Person', props={'name': 'Emil Eifrem', 'born': 1978}),\n",
+       " Node(id='TheMatrixReloaded', label='Movie', props={'title': 'The Matrix Reloaded', 'released': 2003, 'tagline': 'Free your mind'})]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodes[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ab66884a-5174-437c-91a2-d822366ba16b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame.from_records([{\"id\": node.id, \"label\": node.label, \"props\": node.props} for node in nodes]).set_index(\"id\").to_pickle(\"nodes.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "188e259e-b56f-4c81-9a5a-a23f63ba1146",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame.from_records([{\"id\": edge.id, \"label\": edge.label, \"props\": edge.props} for edge in edges]).set_index(\"id\").to_pickle(\"edges.pkl\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap10",
+   "language": "python",
+   "name": "chap10"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter10/01_Neo4j_bindings.ipynb b/Chapter10/01_Neo4j_bindings.ipynb
new file mode 100644
index 0000000..ad3cd94
--- /dev/null
+++ b/Chapter10/01_Neo4j_bindings.ipynb
@@ -0,0 +1,747 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph Database Connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following, we will show you how to connect and query data on Neo4j, using python. \n",
+    "\n",
+    "**IMPORTANT NOTE**\n",
+    "\n",
+    "This notebook requires that you have access to a working version of Neo4j. In order to install Neo4j locally, we advise you to refer to the Neo4j webpage (https://neo4j.com/download/) or to use docker (https://hub.docker.com/_/neo4j)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./movieCreationQuery.txt\", \"rb\") as fid:\n",
+    "    lines = fid.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \" \".join([line.decode(\"utf-8\").replace(\"\\n\", \"\") for line in lines])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neo4j import GraphDatabase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "host = os.environ.get(\"NEO4J_HOST\", \"localhost\")\n",
+    "\n",
+    "uri = f\"neo4j://{host}:7687\"\n",
+    "driver = GraphDatabase.driver(uri, auth=(\"neo4j\", \"neo5j\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_query(tx, query):\n",
+    "    return list(tx.run(query))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1298248/469611421.py:2: DeprecationWarning: write_transaction has been renamed to execute_write\n",
+      "  session.write_transaction(run_query, query)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with driver.session() as session:\n",
+    "    session.write_transaction(run_query, query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"MATCH (n) RETURN count(*)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1298248/3167206639.py:2: DeprecationWarning: read_transaction has been renamed to execute_read\n",
+      "  result = session.read_transaction(run_query, query)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<Record count(*)=171>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with driver.session() as session:\n",
+    "    result = session.read_transaction(run_query, query)\n",
+    "[r for r in result]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using `graphdatascience`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deusebio/.pyenv/versions/graph-machine-learning-310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import graphdatascience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphdatascience import GraphDataScience\n",
+    "\n",
+    "import os\n",
+    "host = os.environ.get(\"NEO4J_HOST\", \"localhost\")\n",
+    "\n",
+    "uri = f\"bolt://{host}:7687\"\n",
+    "gds = GraphDataScience(uri, auth=(\"neo4j\", \"neo5j\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count(*)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>171</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count(*)\n",
+       "0       171"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.run_cypher(\"MATCH (n) RETURN count(*);\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the analytics capabilities of `graphdatascience`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = gds.graph.load_cora()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>degreeDistribution</th>\n",
+       "      <th>graphName</th>\n",
+       "      <th>database</th>\n",
+       "      <th>databaseLocation</th>\n",
+       "      <th>memoryUsage</th>\n",
+       "      <th>sizeInBytes</th>\n",
+       "      <th>nodeCount</th>\n",
+       "      <th>relationshipCount</th>\n",
+       "      <th>configuration</th>\n",
+       "      <th>density</th>\n",
+       "      <th>creationTime</th>\n",
+       "      <th>modificationTime</th>\n",
+       "      <th>schema</th>\n",
+       "      <th>schemaWithOrientation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>{'min': 0, 'max': 166, 'p90': 5, 'p999': 74, '...</td>\n",
+       "      <td>cora</td>\n",
+       "      <td>neo4j</td>\n",
+       "      <td>local</td>\n",
+       "      <td>34 MiB</td>\n",
+       "      <td>35685078</td>\n",
+       "      <td>2708</td>\n",
+       "      <td>5429</td>\n",
+       "      <td>{'readConcurrency': 4, 'undirectedRelationship...</td>\n",
+       "      <td>0.000741</td>\n",
+       "      <td>2025-02-23T17:46:21.127305017+00:00</td>\n",
+       "      <td>2025-02-23T17:46:21.127305017+00:00</td>\n",
+       "      <td>{'graphProperties': {}, 'nodes': {'Paper': {'s...</td>\n",
+       "      <td>{'graphProperties': {}, 'nodes': {'Paper': {'s...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  degreeDistribution graphName database  \\\n",
+       "0  {'min': 0, 'max': 166, 'p90': 5, 'p999': 74, '...      cora    neo4j   \n",
+       "\n",
+       "  databaseLocation memoryUsage  sizeInBytes  nodeCount  relationshipCount  \\\n",
+       "0            local      34 MiB     35685078       2708               5429   \n",
+       "\n",
+       "                                       configuration   density  \\\n",
+       "0  {'readConcurrency': 4, 'undirectedRelationship...  0.000741   \n",
+       "\n",
+       "                          creationTime                     modificationTime  \\\n",
+       "0  2025-02-23T17:46:21.127305017+00:00  2025-02-23T17:46:21.127305017+00:00   \n",
+       "\n",
+       "                                              schema  \\\n",
+       "0  {'graphProperties': {}, 'nodes': {'Paper': {'s...   \n",
+       "\n",
+       "                               schemaWithOrientation  \n",
+       "0  {'graphProperties': {}, 'nodes': {'Paper': {'s...  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G=gds.graph.get(\"cora\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Paper    [subject, features]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.node_properties()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nodeId</th>\n",
+       "      <th>propertyValue</th>\n",
+       "      <th>nodeLabels</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31336</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1061127</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1106406</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13195</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37879</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2703</th>\n",
+       "      <td>1128975</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2704</th>\n",
+       "      <td>1128977</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2705</th>\n",
+       "      <td>1128978</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2706</th>\n",
+       "      <td>117328</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2707</th>\n",
+       "      <td>24043</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2708 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       nodeId  propertyValue nodeLabels\n",
+       "0       31336              0         []\n",
+       "1     1061127              1         []\n",
+       "2     1106406              2         []\n",
+       "3       13195              2         []\n",
+       "4       37879              3         []\n",
+       "...       ...            ...        ...\n",
+       "2703  1128975              5         []\n",
+       "2704  1128977              5         []\n",
+       "2705  1128978              5         []\n",
+       "2706   117328              6         []\n",
+       "2707    24043              0         []\n",
+       "\n",
+       "[2708 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.nodeProperty.stream(gds.graph.get(\"cora\"), node_property=\"subject\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pr_result = gds.pageRank.mutate(G, mutateProperty=\"pagerank\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compute millis: 23\n",
+      "Node properties written: 2708\n",
+      "Centrality distribution: {'min': 0.14999961853027344, 'max': 3.5378417968749996, 'p90': 0.4555196762084961, 'p999': 2.6002798080444336, 'p99': 1.5071401596069336, 'p50': 0.21511173248291016, 'p75': 0.3093576431274414, 'p95': 0.6003026962280273, 'mean': 0.2869838661069884}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Compute millis: {pr_result['computeMillis']}\")\n",
+    "print(f\"Node properties written: {pr_result['nodePropertiesWritten']}\")\n",
+    "print(f\"Centrality distribution: {pr_result['centralityDistribution']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Paper    [pagerank, subject, features]\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.node_properties()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nodeId</th>\n",
+       "      <th>pagerank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.203022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40</td>\n",
+       "      <td>0.168341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>114</td>\n",
+       "      <td>0.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>117</td>\n",
+       "      <td>0.150000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>128</td>\n",
+       "      <td>0.184487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2703</th>\n",
+       "      <td>1154500</td>\n",
+       "      <td>0.161591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2704</th>\n",
+       "      <td>1154520</td>\n",
+       "      <td>0.168214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2705</th>\n",
+       "      <td>1154524</td>\n",
+       "      <td>0.307409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2706</th>\n",
+       "      <td>1154525</td>\n",
+       "      <td>0.248215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2707</th>\n",
+       "      <td>1155073</td>\n",
+       "      <td>0.601702</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2708 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       nodeId  pagerank\n",
+       "0          35  0.203022\n",
+       "1          40  0.168341\n",
+       "2         114  0.150000\n",
+       "3         117  0.150000\n",
+       "4         128  0.184487\n",
+       "...       ...       ...\n",
+       "2703  1154500  0.161591\n",
+       "2704  1154520  0.168214\n",
+       "2705  1154524  0.307409\n",
+       "2706  1154525  0.248215\n",
+       "2707  1155073  0.601702\n",
+       "\n",
+       "[2708 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.nodeProperties.stream(G, [\"pagerank\"], separate_property_columns=True)\n",
+    "# gds.graph.nodeProperties.write(G, [\"pagerank\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "graphName                                                             cora\n",
+       "database                                                             neo4j\n",
+       "databaseLocation                                                     local\n",
+       "memoryUsage                                                               \n",
+       "sizeInBytes                                                             -1\n",
+       "nodeCount                                                             2708\n",
+       "relationshipCount                                                     5429\n",
+       "configuration            {'readConcurrency': 4, 'undirectedRelationship...\n",
+       "density                                                           0.000741\n",
+       "creationTime                           2025-02-23T17:46:21.127305017+00:00\n",
+       "modificationTime                       2025-02-23T17:46:21.352154457+00:00\n",
+       "schema                   {'graphProperties': {}, 'nodes': {'Paper': {'p...\n",
+       "schemaWithOrientation    {'graphProperties': {}, 'nodes': {'Paper': {'p...\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gds.graph.drop(\"cora\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1298248/1407641033.py:2: DeprecationWarning: write_transaction has been renamed to execute_write\n",
+      "  result = session.write_transaction(run_query, \"MATCH (n)-[e]-() DELETE n, e\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "with driver.session() as session:\n",
+    "    result = session.write_transaction(run_query, \"MATCH (n)-[e]-() DELETE n, e\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap10",
+   "language": "python",
+   "name": "chap10"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter10/02_JanusGraph_Gremlin.ipynb b/Chapter10/02_JanusGraph_Gremlin.ipynb
new file mode 100644
index 0000000..6b4e3bc
--- /dev/null
+++ b/Chapter10/02_JanusGraph_Gremlin.ipynb
@@ -0,0 +1,1081 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "418dbae3-2817-4b04-b99d-50b1c67968fa",
+   "metadata": {},
+   "source": [
+    "# JanusGraph and Gremlin queries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e2325c8-72ce-43f6-bcd0-e6bb681ee386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gremlin_python import statics\n",
+    "from gremlin_python.structure.graph import Graph\n",
+    "from gremlin_python.process.graph_traversal import __\n",
+    "from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection\n",
+    "from gremlin_python.driver.serializer import GraphSONSerializersV3d0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "caa8be3b-c448-4918-a531-6072a23e1c14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ed4de73d-e4bc-45e5-bf9a-7abfc090c7e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gremlin_python.process.anonymous_traversal import traversal\n",
+    "\n",
+    "import os\n",
+    "host = os.environ.get(\"JANUSGRAPH_HOST\", \"localhost\")\n",
+    "\n",
+    "connection = DriverRemoteConnection(f\"ws://{host}:8182/gremlin\", \"g\", message_serializer=GraphSONSerializersV3d0())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1b6aeb8c-6a8e-4866-b49c-1ed6cdc373be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = Graph()\n",
+    "g = graph.traversal().withRemote(connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d1e5e05a-b09a-444e-ad7e-3de685ea10be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v[8272]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.addV('student').property('name', 'Jeffery').property('GPA', 4.0).next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "db401998-d513-4097-8c8a-2d74c4eeddc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v[4304]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.addV('student').property('name', 'Robert').property('GPA', 3.0).next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d4e5e347-0298-46a6-9d13-971b66bd7211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v1, v2 = g.V().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cedea689-8e8e-424b-b935-c2422f2037e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v[8272]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e3fdf108-b8f1-48d7-84aa-4f6f333ceedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "v[4304]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "345de3a5-59d2-4710-aaf7-f8923d25531c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "e[{'@type': 'janusgraph:RelationIdentifier', '@value': {'relationId': '3yi-6ds-36d-3bk'}}][8272-FRIEND_OF->4304]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.addE(\"FRIEND_OF\").from_(v1).to(v2).property(\"since\", \"2014\").next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4aebd7dc-da82-42d3-ba52-877c258cfa65",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['addV', 'student'], ['property', 'name', 'Claire'], ['property', 'GPA', 3.9], ['as', 'n1'], ['addV', 'student'], ['property', 'name', 'Lisa'], ['property', 'GPA', 3.6], ['as', 'n2'], ['addE', 'FRIEND_OF'], ['from', 'n1'], ['to', 'n2'], ['property', 'since', '2014'], ['none'], ['values', '_ipython_canary_method_should_not_exist_'], ['values', '_ipython_canary_method_should_not_exist_']]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g\\\n",
+    "  .addV('student').property('name', 'Claire').property('GPA', 3.9).as_(\"n1\")\\\n",
+    "  .addV('student').property('name', 'Lisa').property('GPA', 3.6).as_(\"n2\")\\\n",
+    "  .addE(\"FRIEND_OF\").from_(\"n1\").to(\"n2\").property(\"since\", \"2014\")\\\n",
+    "  .iterate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6f454c96-9775-4469-b0ea-596dde823e0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[v[8272], v[4304], v[8400], v[8416]]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "607a9c7c-4f9f-4921-baeb-aada093976c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[e[{'@type': 'janusgraph:RelationIdentifier', '@value': {'relationId': '3yi-6ds-36d-3bk'}}][8272-FRIEND_OF->4304],\n",
+       " e[{'@type': 'janusgraph:RelationIdentifier', '@value': {'relationId': '3kq-6hc-36d-6hs'}}][8400-FRIEND_OF->8416]]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.E().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "49fc830c-c265-4643-8ed2-6c83b4a64af9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().drop().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b0f36ccb-5c18-4045-903a-3ee0b90b5d1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.E().drop().to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d054308-1549-4642-a7f5-012f19bc27f3",
+   "metadata": {},
+   "source": [
+    "### Import Karate Club Graph "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "deeb8022-6d3f-4724-8420-621282b6aa87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bad6ce27-f43d-45bf-932a-3cdf93b2bf53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f6388ad9-0952-444e-92f2-2ddc9560140f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = networkx.karate_club_graph().nodes\n",
+    "nodes = pd.DataFrame.from_records([{\"id\": node} | nodes[node] for node in nodes]).set_index(\"id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "61d75876-548e-4c5c-9561-ee948b620442",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = networkx.karate_club_graph().edges\n",
+    "edges = pd.DataFrame.from_records([{\"id\": edge} | edges[edge] for edge in edges]).set_index(\"id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bd84322d-76d1-4fa1-845b-c04987df595e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>club</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mr. Hi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mr. Hi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mr. Hi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mr. Hi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mr. Hi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      club\n",
+       "id        \n",
+       "0   Mr. Hi\n",
+       "1   Mr. Hi\n",
+       "2   Mr. Hi\n",
+       "3   Mr. Hi\n",
+       "4   Mr. Hi"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodes.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "04f5f159-3796-4e35-9960-8452fad83a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(0, 1)</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 2)</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 3)</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 4)</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 5)</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        weight\n",
+       "id            \n",
+       "(0, 1)       4\n",
+       "(0, 2)       5\n",
+       "(0, 3)       3\n",
+       "(0, 4)       3\n",
+       "(0, 5)       3"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64ed31a0-8938-45ab-8f53-6a37271e5788",
+   "metadata": {},
+   "source": [
+    "Graph Generation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "478cd590-89e9-4cf3-b82d-ac4d1d743856",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gremlin_python.process.graph_traversal import GraphTraversalSource"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1f6351f5-91fc-40d5-bc30-177796e36a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import reduce\n",
+    "\n",
+    "def build_node_query(agg: GraphTraversalSource, id: str, label: str, properties:dict):\n",
+    "    id_str = str(id)\n",
+    "    agg = agg.add_v(label).property(\"id\", id_str)\n",
+    "    for k, v in properties.items():\n",
+    "        agg.property(k, v)\n",
+    "    return agg.as_(f\"n_{id_str}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e3ebf002-2965-4049-bc22-4b326bd10b5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_edge_query(agg: GraphTraversalSource, id: tuple[str,str], label: str, properties:dict):\n",
+    "    source_str = str(id[0])\n",
+    "    target_str = str(id[1])\n",
+    "    edge = agg\\\n",
+    "        .V().has(\"id\", str(source_str)).as_(\"source\")\\\n",
+    "        .V().has(\"id\", str(target_str)).as_(\"target\")\\\n",
+    "        .addE(label).from_(\"source\").to(\"target\")\n",
+    "    for k, v in properties.items():\n",
+    "        edge.property(k, v)\n",
+    "    return edge.as_(f\"edge_{source_str}_{target_str}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "676a24c7-139b-4622-9316-4c531cbfb73c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = reduce(lambda g, node: build_node_query(g, node[0], \"Person\", node[1].to_dict()), nodes.iterrows(), g).iterate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9b9367df-bbab-4d44-b9d5-e504751e21bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = reduce(lambda g, edge: build_edge_query(g, edge[0], \"FRIEND_OF\", edge[1].to_dict()), edges.iterrows(), g).iterate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6395d565-a5df-4c77-a68b-c8c5e7b26203",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().has(\"club\", \"Mr. Hi\").out(\"FRIEND_OF\").has(\"club\", 'Officer').count().next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "21d5c7bd-dfaa-4dbf-a1b0-25b957c32f11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['30', '30', '33', '33', '33', '31', '9', '27', '28', '32', '32']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().has(\"club\", \"Mr. Hi\").out(\"FRIEND_OF\").has(\"club\", 'Officer').values(\"id\").to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "220ef136-3740-4101-b9f4-ca698343a9de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2', '2', '2', '2', '8', '8', '8', '0', '1', '13', '19']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().has(\"club\", \"Officer\").in_(\"FRIEND_OF\").has(\"club\", 'Mr. Hi').values(\"id\").to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cac322ee-de5f-47da-8c24-1b27c3fc89c8",
+   "metadata": {},
+   "source": [
+    "### Drop databases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "021e1a57-abe0-4db0-a8b8-a3cfc033e448",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().drop().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ffc6f2bf-ff3b-4bee-a2e6-65cb2df61fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.E().drop().to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b3deb6d-6893-4bd5-8be3-87d3c2e49d27",
+   "metadata": {},
+   "source": [
+    "### Import Movie Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "25a310ff-10cb-4eaa-9677-4aa91f96d0c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = pd.read_pickle(\"nodes.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f3c0e8d7-d837-4e1a-8369-81aebc0924f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edges = pd.read_pickle(\"edges.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "28289b7d-b281-493b-a231-557c012edbc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>props</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TheMatrix</th>\n",
+       "      <td>Movie</td>\n",
+       "      <td>{'title': 'The Matrix', 'released': 1999, 'tag...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Keanu</th>\n",
+       "      <td>Person</td>\n",
+       "      <td>{'name': 'Keanu Reeves', 'born': 1964}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Carrie</th>\n",
+       "      <td>Person</td>\n",
+       "      <td>{'name': 'Carrie-Anne Moss', 'born': 1967}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Laurence</th>\n",
+       "      <td>Person</td>\n",
+       "      <td>{'name': 'Laurence Fishburne', 'born': 1961}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hugo</th>\n",
+       "      <td>Person</td>\n",
+       "      <td>{'name': 'Hugo Weaving', 'born': 1960}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            label                                              props\n",
+       "id                                                                  \n",
+       "TheMatrix   Movie  {'title': 'The Matrix', 'released': 1999, 'tag...\n",
+       "Keanu      Person             {'name': 'Keanu Reeves', 'born': 1964}\n",
+       "Carrie     Person         {'name': 'Carrie-Anne Moss', 'born': 1967}\n",
+       "Laurence   Person       {'name': 'Laurence Fishburne', 'born': 1961}\n",
+       "Hugo       Person             {'name': 'Hugo Weaving', 'born': 1960}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodes.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "68cf9037-8b98-4ed7-94de-bb14c8b678f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>props</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(Keanu, TheMatrix)</th>\n",
+       "      <td>ACTED_IN</td>\n",
+       "      <td>{'roles': '[\"Neo\"]'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(Carrie, TheMatrix)</th>\n",
+       "      <td>ACTED_IN</td>\n",
+       "      <td>{'roles': '[\"Trinity\"]'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(Laurence, TheMatrix)</th>\n",
+       "      <td>ACTED_IN</td>\n",
+       "      <td>{'roles': '[\"Morpheus\"]'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(Hugo, TheMatrix)</th>\n",
+       "      <td>ACTED_IN</td>\n",
+       "      <td>{'roles': '[\"Agent Smith\"]'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LillyW, TheMatrix)</th>\n",
+       "      <td>DIRECTED</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          label                         props\n",
+       "id                                                           \n",
+       "(Keanu, TheMatrix)     ACTED_IN          {'roles': '[\"Neo\"]'}\n",
+       "(Carrie, TheMatrix)    ACTED_IN      {'roles': '[\"Trinity\"]'}\n",
+       "(Laurence, TheMatrix)  ACTED_IN     {'roles': '[\"Morpheus\"]'}\n",
+       "(Hugo, TheMatrix)      ACTED_IN  {'roles': '[\"Agent Smith\"]'}\n",
+       "(LillyW, TheMatrix)    DIRECTED                            {}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78e8d550-e049-4de6-842f-39872e4d6f98",
+   "metadata": {},
+   "source": [
+    "Creation of edges and nodes batch by batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a80cc0ee-c6af-44f3-9f73-f43a8f538ffc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import islice\n",
+    "\n",
+    "def batched(iterable, n):\n",
+    "    \"Batch data into lists of length n. The last batch may be shorter.\"\n",
+    "    # batched('ABCDEFG', 3) --> ABC DEF G\n",
+    "    it = iter(iterable)\n",
+    "    while True:\n",
+    "        batch = list(islice(it, n))\n",
+    "        if not batch:\n",
+    "            return\n",
+    "        yield batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "651314fa-efea-4fb7-9a7f-6487fcde6a9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_from_batch(builder, iterable, batch_size):\n",
+    "    for batch in batched(iterable, batch_size):\n",
+    "        _ = reduce(lambda g, item: builder(g, item[0], item[1][\"label\"], item[1][\"props\"]), batch, g).iterate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "c331abcb-63b2-4902-bf75-9c0845f2219b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_from_batch(build_node_query, nodes.iterrows(), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "224412d2-c0f7-46b3-861f-6ead80084ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "create_from_batch(build_edge_query, edges.iterrows(), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "43844ee1-c300-4ed7-a0cc-5e7922db222d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "171"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().count().next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a9bc20b4-d8f7-414b-985d-4f85ecc16de2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "253"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.E().count().next()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7675a693-2c69-4e01-91ff-6fccf3d050d7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Emil Eifrem',\n",
+       " 'Carrie-Anne Moss',\n",
+       " 'Laurence Fishburne',\n",
+       " 'Keanu Reeves',\n",
+       " 'Hugo Weaving',\n",
+       " 'Charlize Theron',\n",
+       " 'Al Pacino',\n",
+       " 'Gene Hackman',\n",
+       " 'Brooke Langton',\n",
+       " 'Orlando Jones',\n",
+       " 'Takeshi Kitano',\n",
+       " 'Dina Meyer',\n",
+       " 'Ice-T',\n",
+       " 'Jack Nicholson',\n",
+       " 'Diane Keaton']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().has('Person', 'name', 'Keanu Reeves').out(\"ACTED_IN\").in_(\"ACTED_IN\").values(\"name\").dedup().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fed4e739-f3c9-4b2b-9884-e185f8d390d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.V().drop().to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "77079085-b277-4bf7-9c59-fa1928caf560",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.E().drop().to_list()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap10",
+   "language": "python",
+   "name": "chap10"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Chapter09/dataset/movieCreationQuery.txt b/Chapter10/movieCreationQuery.txt
similarity index 96%
rename from Chapter09/dataset/movieCreationQuery.txt
rename to Chapter10/movieCreationQuery.txt
index 9617449..d27dd47 100644
--- a/Chapter09/dataset/movieCreationQuery.txt
+++ b/Chapter10/movieCreationQuery.txt
@@ -38,7 +38,7 @@ CREATE
 (LanaW)-[:DIRECTED]->(TheMatrixRevolutions),
 (JoelS)-[:PRODUCED]->(TheMatrixRevolutions)
 
-CREATE (TheDevilsAdvocate:Movie {title:"The Devil's Advocate", released:1997, tagline:'Evil has its winning ways'})
+CREATE (TheDevilsAdvocate:Movie {title:"The Devil's Advocate", released:1997, tagline:"Evil has its winning ways"})
 CREATE (Charlize:Person {name:'Charlize Theron', born:1975})
 CREATE (Al:Person {name:'Al Pacino', born:1940})
 CREATE (Taylor:Person {name:'Taylor Hackford', born:1944})
@@ -78,7 +78,7 @@ CREATE
 (RobR)-[:DIRECTED]->(AFewGoodMen),
 (AaronS)-[:WROTE]->(AFewGoodMen)
 
-CREATE (TopGun:Movie {title:"Top Gun", released:1986, tagline:'I feel the need, the need for speed.'})
+CREATE (TopGun:Movie {title:"Top Gun", released:1986, tagline:"I feel the need, the need for speed."})
 CREATE (KellyM:Person {name:'Kelly McGillis', born:1957})
 CREATE (ValK:Person {name:'Val Kilmer', born:1959})
 CREATE (AnthonyE:Person {name:'Anthony Edwards', born:1962})
@@ -172,7 +172,7 @@ CREATE
 (JamesC)-[:ACTED_IN {roles:['Judge Fielding']}]->(SnowFallingonCedars),
 (ScottH)-[:DIRECTED]->(SnowFallingonCedars)
 
-CREATE (YouveGotMail:Movie {title:"You've Got Mail", released:1998, tagline:'At odds in life... in love on-line.'})
+CREATE (YouveGotMail:Movie {title:"You've Got Mail", released:1998, tagline:"At odds in life... in love on-line."})
 CREATE (ParkerP:Person {name:'Parker Posey', born:1968})
 CREATE (DaveC:Person {name:'Dave Chappelle', born:1973})
 CREATE (SteveZ:Person {name:'Steve Zahn', born:1967})
@@ -244,7 +244,7 @@ CREATE
 (Orlando)-[:ACTED_IN {roles:['Clifford Franklin']}]->(TheReplacements),
 (Howard)-[:DIRECTED]->(TheReplacements)
 
-CREATE (RescueDawn:Movie {title:'RescueDawn', released:2006, tagline:"Based on the extraordinary true story of one man's fight for freedom"})
+CREATE (RescueDawn:Movie {title:"RescueDawn", released:2006, tagline:"Based on the extraordinary true story of one man's fight for freedom"})
 CREATE (ChristianB:Person {name:'Christian Bale', born:1974})
 CREATE (ZachG:Person {name:'Zach Grenier', born:1954})
 CREATE
@@ -262,7 +262,7 @@ CREATE
 (Gene)-[:ACTED_IN {roles:['Sen. Kevin Keeley']}]->(TheBirdcage),
 (MikeN)-[:DIRECTED]->(TheBirdcage)
 
-CREATE (Unforgiven:Movie {title:'Unforgiven', released:1992, tagline:"It's a hell of a thing, killing a man"})
+CREATE (Unforgiven:Movie {title:"Unforgiven", released:1992, tagline:"It's a hell of a thing, killing a man"})
 CREATE (RichardH:Person {name:'Richard Harris', born:1930})
 CREATE (ClintE:Person {name:'Clint Eastwood', born:1930})
 CREATE
@@ -363,7 +363,7 @@ CREATE
 (LanaW)-[:PRODUCED]->(NinjaAssassin),
 (JoelS)-[:PRODUCED]->(NinjaAssassin)
 
-CREATE (TheGreenMile:Movie {title:'The Green Mile', released:1999, tagline:"Walk a mile you'll never forget."})
+CREATE (TheGreenMile:Movie {title:"The Green Mile", released:1999, tagline:"Walk a mile you'll never forget."})
 CREATE (MichaelD:Person {name:'Michael Clarke Duncan', born:1957})
 CREATE (DavidM:Person {name:'David Morse', born:1953})
 CREATE (SamR:Person {name:'Sam Rockwell', born:1968})
@@ -373,10 +373,10 @@ CREATE (FrankD:Person {name:'Frank Darabont', born:1959})
 CREATE
 (TomH)-[:ACTED_IN {roles:['Paul Edgecomb']}]->(TheGreenMile),
 (MichaelD)-[:ACTED_IN {roles:['John Coffey']}]->(TheGreenMile),
-(DavidM)-[:ACTED_IN {roles:['Brutus "Brutal" Howell']}]->(TheGreenMile),
+(DavidM)-[:ACTED_IN {roles:["Brutus 'Brutal' Howell"]}]->(TheGreenMile),
 (BonnieH)-[:ACTED_IN {roles:['Jan Edgecomb']}]->(TheGreenMile),
 (JamesC)-[:ACTED_IN {roles:['Warden Hal Moores']}]->(TheGreenMile),
-(SamR)-[:ACTED_IN {roles:['"Wild Bill" Wharton']}]->(TheGreenMile),
+(SamR)-[:ACTED_IN {roles:["'Wild Bill' Wharton"]}]->(TheGreenMile),
 (GaryS)-[:ACTED_IN {roles:['Burt Hammersmith']}]->(TheGreenMile),
 (PatriciaC)-[:ACTED_IN {roles:['Melinda Moores']}]->(TheGreenMile),
 (FrankD)-[:DIRECTED]->(TheGreenMile)
@@ -393,14 +393,14 @@ CREATE
 (SamR)-[:ACTED_IN {roles:['James Reston, Jr.']}]->(FrostNixon),
 (RonH)-[:DIRECTED]->(FrostNixon)
 
-CREATE (Hoffa:Movie {title:'Hoffa', released:1992, tagline:"He didn't want law. He wanted justice."})
+CREATE (Hoffa:Movie {title:"Hoffa", released:1992, tagline:"He didn't want law. He wanted justice."})
 CREATE (DannyD:Person {name:'Danny DeVito', born:1944})
 CREATE (JohnR:Person {name:'John C. Reilly', born:1965})
 CREATE
 (JackN)-[:ACTED_IN {roles:['Hoffa']}]->(Hoffa),
-(DannyD)-[:ACTED_IN {roles:['Robert "Bobby" Ciaro']}]->(Hoffa),
+(DannyD)-[:ACTED_IN {roles:["Robert 'Bobby' Ciaro"]}]->(Hoffa),
 (JTW)-[:ACTED_IN {roles:['Frank Fitzsimmons']}]->(Hoffa),
-(JohnR)-[:ACTED_IN {roles:['Peter "Pete" Connelly']}]->(Hoffa),
+(JohnR)-[:ACTED_IN {roles:["Peter 'Pete' Connelly"]}]->(Hoffa),
 (DannyD)-[:DIRECTED]->(Hoffa)
 
 CREATE (Apollo13:Movie {title:'Apollo 13', released:1995, tagline:'Houston, we have a problem.'})
@@ -414,14 +414,14 @@ CREATE
 (GaryS)-[:ACTED_IN {roles:['Ken Mattingly']}]->(Apollo13),
 (RonH)-[:DIRECTED]->(Apollo13)
 
-CREATE (Twister:Movie {title:'Twister', released:1996, tagline:"Don't Breathe. Don't Look Back."})
+CREATE (Twister:Movie {title:"Twister", released:1996, tagline:"Don't Breathe. Don't Look Back."})
 CREATE (PhilipH:Person {name:'Philip Seymour Hoffman', born:1967})
 CREATE (JanB:Person {name:'Jan de Bont', born:1943})
 CREATE
 (BillPax)-[:ACTED_IN {roles:['Bill Harding']}]->(Twister),
 (HelenH)-[:ACTED_IN {roles:['Dr. Jo Harding']}]->(Twister),
 (ZachG)-[:ACTED_IN {roles:['Eddie']}]->(Twister),
-(PhilipH)-[:ACTED_IN {roles:['Dustin "Dusty" Davis']}]->(Twister),
+(PhilipH)-[:ACTED_IN {roles:["Dustin 'Dusty' Davis"]}]->(Twister),
 (JanB)-[:DIRECTED]->(Twister)
 
 CREATE (CastAway:Movie {title:'Cast Away', released:2000, tagline:'At the edge of the world, his journey begins.'})
@@ -449,7 +449,7 @@ CREATE
 (NancyM)-[:PRODUCED]->(SomethingsGottaGive),
 (NancyM)-[:WROTE]->(SomethingsGottaGive)
 
-CREATE (BicentennialMan:Movie {title:'Bicentennial Man', released:1999, tagline:"One robot's 200 year journey to become an ordinary man."})
+CREATE (BicentennialMan:Movie {title:"Bicentennial Man", released:1999, tagline:"One robot's 200 year journey to become an ordinary man."})
 CREATE (ChrisC:Person {name:'Chris Columbus', born:1958})
 CREATE
 (Robin)-[:ACTED_IN {roles:['Andrew Marin']}]->(BicentennialMan),
@@ -479,7 +479,7 @@ CREATE
 (GeenaD)-[:ACTED_IN {roles:['Dottie Hinson']}]->(ALeagueofTheirOwn),
 (LoriP)-[:ACTED_IN {roles:['Kit Keller']}]->(ALeagueofTheirOwn),
 (RosieO)-[:ACTED_IN {roles:['Doris Murphy']}]->(ALeagueofTheirOwn),
-(Madonna)-[:ACTED_IN {roles:['"All the Way" Mae Mordabito']}]->(ALeagueofTheirOwn),
+(Madonna)-[:ACTED_IN {roles:["'All the Way' Mae Mordabito"]}]->(ALeagueofTheirOwn),
 (BillPax)-[:ACTED_IN {roles:['Bob Hinson']}]->(ALeagueofTheirOwn),
 (PennyM)-[:DIRECTED]->(ALeagueofTheirOwn)
 
diff --git a/Chapter10/poetry.lock b/Chapter10/poetry.lock
new file mode 100644
index 0000000..f405153
--- /dev/null
+++ b/Chapter10/poetry.lock
@@ -0,0 +1,1884 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "aenum"
+version = "3.1.15"
+description = "Advanced Enumerations (compatible with Python's stdlib Enum), NamedTuples, and NamedConstants"
+optional = false
+python-versions = "*"
+files = [
+    {file = "aenum-3.1.15-py2-none-any.whl", hash = "sha256:27b1710b9d084de6e2e695dab78fe9f269de924b51ae2850170ee7e1ca6288a5"},
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+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.4"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[[package]]
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.10"
+content-hash = "b294cb00887149f4bba19e09a290c509535ce8375c270266e7bf385e594d5594"
diff --git a/Chapter10/pyproject.toml b/Chapter10/pyproject.toml
new file mode 100644
index 0000000..cc1ce66
--- /dev/null
+++ b/Chapter10/pyproject.toml
@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "Graph Machine Learning - Chapter 10"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "~3.10"
+ipykernel = ">=6.0.0" 
+networkx = ">=3.2.0" 
+neo4j = ">=4.2.0"
+gremlinpython = ">=3.4.6"
+graphdatascience = "^1.14"
+pandas = "^2.2.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/Chapter10/requirements.txt b/Chapter10/requirements.txt
new file mode 100644
index 0000000..3b99f44
--- /dev/null
+++ b/Chapter10/requirements.txt
@@ -0,0 +1,64 @@
+aenum==3.1.15 ; python_version == "3.10"
+aiohappyeyeballs==2.4.4 ; python_version == "3.10"
+aiohttp==3.11.11 ; python_version == "3.10"
+aiosignal==1.3.2 ; python_version == "3.10"
+appnope==0.1.4 ; platform_system == "Darwin" and python_version == "3.10"
+asttokens==3.0.0 ; python_version == "3.10"
+async-timeout==4.0.3 ; python_version == "3.10"
+attrs==24.3.0 ; python_version == "3.10"
+certifi==2025.1.31 ; python_version == "3.10"
+cffi==1.17.1 ; implementation_name == "pypy" and python_version == "3.10"
+charset-normalizer==3.4.1 ; python_version == "3.10"
+colorama==0.4.6 ; python_version == "3.10" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version == "3.10"
+debugpy==1.8.11 ; python_version == "3.10"
+decorator==5.1.1 ; python_version == "3.10"
+exceptiongroup==1.2.2 ; python_version == "3.10"
+executing==2.1.0 ; python_version == "3.10"
+frozenlist==1.5.0 ; python_version == "3.10"
+graphdatascience==1.14 ; python_version == "3.10"
+gremlinpython==3.7.3 ; python_version == "3.10"
+idna==3.10 ; python_version == "3.10"
+ipykernel==6.29.5 ; python_version == "3.10"
+ipython==8.31.0 ; python_version == "3.10"
+isodate==0.7.2 ; python_version == "3.10"
+jedi==0.19.2 ; python_version == "3.10"
+jupyter-client==8.6.3 ; python_version == "3.10"
+jupyter-core==5.7.2 ; python_version == "3.10"
+matplotlib-inline==0.1.7 ; python_version == "3.10"
+multidict==6.1.0 ; python_version == "3.10"
+multimethod==2.0 ; python_version == "3.10"
+neo4j==5.27.0 ; python_version == "3.10"
+nest-asyncio==1.6.0 ; python_version == "3.10"
+networkx==3.4.2 ; python_version == "3.10"
+numpy==2.2.3 ; python_version == "3.10"
+packaging==24.2 ; python_version == "3.10"
+pandas==2.2.3 ; python_version == "3.10"
+parso==0.8.4 ; python_version == "3.10"
+pexpect==4.9.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10"
+platformdirs==4.3.6 ; python_version == "3.10"
+prompt-toolkit==3.0.48 ; python_version == "3.10"
+propcache==0.2.1 ; python_version == "3.10"
+psutil==6.1.1 ; python_version == "3.10"
+ptyprocess==0.7.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10"
+pure-eval==0.2.3 ; python_version == "3.10"
+pyarrow==19.0.1 ; python_version == "3.10"
+pycparser==2.22 ; implementation_name == "pypy" and python_version == "3.10"
+pygments==2.19.0 ; python_version == "3.10"
+python-dateutil==2.9.0.post0 ; python_version == "3.10"
+pytz==2024.2 ; python_version == "3.10"
+pywin32==308 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version == "3.10"
+pyzmq==26.2.0 ; python_version == "3.10"
+requests==2.32.3 ; python_version == "3.10"
+six==1.17.0 ; python_version == "3.10"
+stack-data==0.6.3 ; python_version == "3.10"
+tenacity==9.0.0 ; python_version == "3.10"
+textdistance==4.6.3 ; python_version == "3.10"
+tornado==6.4.2 ; python_version == "3.10"
+tqdm==4.67.1 ; python_version == "3.10"
+traitlets==5.14.3 ; python_version == "3.10"
+typing-extensions==4.12.2 ; python_version == "3.10"
+tzdata==2025.1 ; python_version == "3.10"
+urllib3==2.3.0 ; python_version == "3.10"
+wcwidth==0.2.13 ; python_version == "3.10"
+yarl==1.18.3 ; python_version == "3.10"
diff --git a/Chapter12/LLM_and_Graphs.ipynb b/Chapter12/LLM_and_Graphs.ipynb
new file mode 100644
index 0000000..90dac8f
--- /dev/null
+++ b/Chapter12/LLM_and_Graphs.ipynb
@@ -0,0 +1,622 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LMM and Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aH3Q56MGDARI"
+   },
+   "source": [
+    "Let's create a toy dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "lDLHAhIlDEvm"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/euler/.conda/envs/chap12/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import torch\n",
+    "\n",
+    "from torch_geometric.data import Data\n",
+    "\n",
+    "# Assume a toy dataset with 3 papers (nodes), edges, and labels\n",
+    "data = Data(\n",
+    "    x=torch.rand(3, 10),  # Random node features\n",
+    "    edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long).t().contiguous(),  # Edges (transposed for PyG)\n",
+    "    y=torch.tensor([0, 1, 2], dtype=torch.long),  # True labels (3 classes)\n",
+    "    text=[\"Paper A abstract about machine learning\", \n",
+    "          \"Paper B abstract about deep learning\", \n",
+    "          \"Paper C abstract about neural networks\"],  # Text data\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset info:\n",
+      "  Number of nodes: 3\n",
+      "  Node feature dimension: 10\n",
+      "  Number of edges: 2\n",
+      "  Number of classes: 3\n",
+      "  True labels: [0, 1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_classes = len(torch.unique(data.y))  # Number of unique classes\n",
+    "\n",
+    "print(f\"Dataset info:\")\n",
+    "print(f\"  Number of nodes: {data.x.size(0)}\")\n",
+    "print(f\"  Node feature dimension: {data.x.size(1)}\")\n",
+    "print(f\"  Number of edges: {data.edge_index.size(1)}\")\n",
+    "print(f\"  Number of classes: {num_classes}\")\n",
+    "print(f\"  True labels: {data.y.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
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+    "id": "YxK0emCj_0ad",
+    "outputId": "f823051a-2dab-49df-b472-5373cde384a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting training...\n",
+      "Iteration 1: GNN Loss = 1.1190, LLM Loss = 0.9824\n",
+      "  GNN predictions: [1, 1, 1]\n",
+      "  LLM predictions: [1, 1, 1]\n",
+      "Iteration 2: GNN Loss = 0.8503, LLM Loss = 1.0020\n",
+      "  GNN predictions: [1, 1, 1]\n",
+      "  LLM predictions: [2, 2, 1]\n",
+      "Iteration 3: GNN Loss = 0.9946, LLM Loss = 0.9380\n",
+      "  GNN predictions: [1, 1, 1]\n",
+      "  LLM predictions: [1, 2, 1]\n",
+      "Iteration 4: GNN Loss = 0.8984, LLM Loss = 0.9884\n",
+      "  GNN predictions: [1, 1, 1]\n",
+      "  LLM predictions: [0, 1, 1]\n",
+      "Iteration 5: GNN Loss = 0.9916, LLM Loss = 0.7875\n",
+      "  GNN predictions: [1, 1, 1]\n",
+      "  LLM predictions: [1, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "from torch_geometric.nn import GCNConv\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# 1. Define the Graph Neural Network (GNN)\n",
+    "class GNN(torch.nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, num_classes):\n",
+    "        super(GNN, self).__init__()\n",
+    "        self.conv1 = GCNConv(input_dim, hidden_dim)\n",
+    "        self.conv2 = GCNConv(hidden_dim, num_classes)  # Output num_classes\n",
+    "        self.dropout = torch.nn.Dropout(0.2)\n",
+    "    \n",
+    "    def forward(self, x, edge_index):\n",
+    "        x = self.conv1(x, edge_index)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.conv2(x, edge_index)\n",
+    "        return x  # Return logits (not softmax)\n",
+    "\n",
+    "# 2. Define the Text Encoder (BERT-based)\n",
+    "class TextEncoder(torch.nn.Module):\n",
+    "    def __init__(self, model_name=\"bert-base-uncased\", num_classes=3):\n",
+    "        super(TextEncoder, self).__init__()\n",
+    "        self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "        self.model = AutoModel.from_pretrained(model_name)\n",
+    "        # Project from BERT's hidden size to number of classes\n",
+    "        self.classifier = torch.nn.Linear(self.model.config.hidden_size, num_classes)\n",
+    "        self.dropout = torch.nn.Dropout(0.1)\n",
+    "    \n",
+    "    def forward(self, texts):\n",
+    "        # Tokenize and encode text data\n",
+    "        inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
+    "        \n",
+    "        with torch.no_grad():  # Freeze BERT parameters during training\n",
+    "            outputs = self.model(**inputs)\n",
+    "        \n",
+    "        # Use [CLS] token embedding\n",
+    "        cls_embedding = outputs.last_hidden_state[:, 0, :]\n",
+    "        cls_embedding = self.dropout(cls_embedding)\n",
+    "        logits = self.classifier(cls_embedding)\n",
+    "        return logits  # Return logits (not softmax)\n",
+    "\n",
+    "\n",
+    "# 4. Training Loop with Bidirectional Pseudo-label Exchange\n",
+    "def train_prediction_alignment(data, gnn, text_encoder, num_iterations=5):\n",
+    "    optimizer_gnn = torch.optim.Adam(gnn.parameters(), lr=0.01)\n",
+    "    optimizer_text = torch.optim.Adam(text_encoder.parameters(), lr=0.0001)\n",
+    "    \n",
+    "    # Initialize with true labels for first iteration\n",
+    "    gnn_pseudo_labels = data.y.clone()\n",
+    "    llm_pseudo_labels = data.y.clone()\n",
+    "    \n",
+    "    for iteration in range(num_iterations):\n",
+    "        # 4.1 Train GNN using LLM pseudo-labels from previous iteration\n",
+    "        gnn.train()\n",
+    "        optimizer_gnn.zero_grad()\n",
+    "        gnn_logits = gnn(data.x, data.edge_index)\n",
+    "        gnn_loss = torch.nn.CrossEntropyLoss()(gnn_logits, llm_pseudo_labels)\n",
+    "        gnn_loss.backward()\n",
+    "        optimizer_gnn.step()\n",
+    "        \n",
+    "        # Generate new GNN pseudo-labels\n",
+    "        with torch.no_grad():\n",
+    "            gnn_pseudo_labels = torch.argmax(gnn_logits, dim=1)\n",
+    "        \n",
+    "        # 4.2 Train Text Encoder using GNN pseudo-labels\n",
+    "        text_encoder.train()\n",
+    "        optimizer_text.zero_grad()\n",
+    "        text_logits = text_encoder(data.text)\n",
+    "        llm_loss = torch.nn.CrossEntropyLoss()(text_logits, gnn_pseudo_labels)\n",
+    "        llm_loss.backward()\n",
+    "        optimizer_text.step()\n",
+    "        \n",
+    "        # Generate new LLM pseudo-labels for next iteration\n",
+    "        with torch.no_grad():\n",
+    "            llm_pseudo_labels = torch.argmax(text_logits, dim=1)\n",
+    "        \n",
+    "        print(f\"Iteration {iteration+1}: GNN Loss = {gnn_loss.item():.4f}, LLM Loss = {llm_loss.item():.4f}\")\n",
+    "        print(f\"  GNN predictions: {gnn_pseudo_labels.tolist()}\")\n",
+    "        print(f\"  LLM predictions: {llm_pseudo_labels.tolist()}\")\n",
+    "\n",
+    "# Initialize models and train\n",
+    "input_dim = data.x.size(1)  # Node feature dimension\n",
+    "hidden_dim = 64\n",
+    "\n",
+    "gnn = GNN(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes)\n",
+    "text_encoder = TextEncoder(num_classes=num_classes)\n",
+    "\n",
+    "print(\"Starting training...\")\n",
+    "train_prediction_alignment(data, gnn, text_encoder, num_iterations=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AwmGzCmH_669",
+    "outputId": "2577a628-8f1c-4efb-c732-1dfbbade06bf"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: Loss = 1.1974958181381226\n",
+      "Epoch 2: Loss = 0.9338013529777527\n",
+      "Epoch 3: Loss = 1.0983785390853882\n",
+      "Epoch 4: Loss = 1.0986661911010742\n",
+      "Epoch 5: Loss = 1.0841368436813354\n",
+      "Epoch 6: Loss = 1.0974050760269165\n",
+      "Epoch 7: Loss = 0.621391773223877\n",
+      "Epoch 8: Loss = 0.5264388918876648\n",
+      "Epoch 9: Loss = 1.1055184602737427\n",
+      "Epoch 10: Loss = 1.0985291004180908\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "from torch_geometric.nn import GraphConv\n",
+    "from torch_geometric.data import Data\n",
+    "\n",
+    "# 1. Define the GNN\n",
+    "class GNN(torch.nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim):\n",
+    "        super(GNN, self).__init__()\n",
+    "        self.conv = GraphConv(input_dim, hidden_dim)\n",
+    "\n",
+    "    def forward(self, x, edge_index):\n",
+    "        return self.conv(x, edge_index)\n",
+    "\n",
+    "# 2. Define the Text Encoder (LLM)\n",
+    "class TextEncoder(torch.nn.Module):\n",
+    "    def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n",
+    "        super(TextEncoder, self).__init__()\n",
+    "        self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "        self.model = AutoModel.from_pretrained(model_name)\n",
+    "        self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n",
+    "\n",
+    "    def forward(self, texts):\n",
+    "        inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n",
+    "        outputs = self.model(**inputs)\n",
+    "        cls_embedding = outputs.last_hidden_state[:, 0, :]  # [CLS] token embedding\n",
+    "        return self.fc(cls_embedding)\n",
+    "\n",
+    "# 3. Contrastive Learning Objective\n",
+    "def contrastive_loss(graph_emb, text_emb, tau=0.1):\n",
+    "    sim = F.cosine_similarity(graph_emb.unsqueeze(1), text_emb.unsqueeze(0), dim=2)\n",
+    "    labels = torch.arange(sim.size(0)).to(sim.device)\n",
+    "    loss = F.cross_entropy(sim / tau, labels)\n",
+    "    return loss\n",
+    "\n",
+    "# 4. Training Loop for Latent Space Alignment\n",
+    "def train_latent_alignment(data, gnn, text_encoder, epochs=10):\n",
+    "    optimizer = torch.optim.Adam(list(gnn.parameters()) + list(text_encoder.parameters()), lr=0.001)\n",
+    "    for epoch in range(epochs):\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        # Encode graph and text\n",
+    "        graph_emb = gnn(data.x, data.edge_index)  # Graph embeddings\n",
+    "        text_emb = text_encoder(data.text)  # Text embeddings\n",
+    "\n",
+    "        # Compute contrastive loss\n",
+    "        loss = contrastive_loss(graph_emb, text_emb)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        print(f\"Epoch {epoch+1}: Loss = {loss.item()}\")\n",
+    "\n",
+    "# 5. Example Data\n",
+    "# Toy data with 3 products and their relationships\n",
+    "data = Data(\n",
+    "    x=torch.rand(3, 10),  # Node features\n",
+    "    edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long),  # Edges\n",
+    "    text=[\"Product A description\", \"Product B description\", \"Product C description\"],  # Text data\n",
+    ")\n",
+    "\n",
+    "# Initialize models and train\n",
+    "gnn = GNN(input_dim=10, hidden_dim=128)\n",
+    "text_encoder = TextEncoder()\n",
+    "train_latent_alignment(data, gnn, text_encoder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mqcZZn6qCCmj"
+   },
+   "source": [
+    "# GraphRAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xM73g-bDiPPG"
+   },
+   "source": [
+    "If using Colab you can simply run the following cells.\n",
+    "\n",
+    "Otherwise, if you want to use the local backend, please:\n",
+    "- download neo4j desktop on [docker](https://neo4j.com/docs/graph-data-science/current/installation/installation-docker/)*\n",
+    "- download [lm-studio](https://lmstudio.ai/) and download the minicpm-llama3-v-2_5 and nomic-embed-text model\n",
+    "\n",
+    "*run docker as:\n",
+    "\n",
+    "\n",
+    "```\n",
+    "docker run --rm --env NEO4J_AUTH=neo4j/defaultpass -p 7474:7474 -p 7687:7687 -v $PWD/data:/data -v $PWD/plugins:/plugins --name neo4j-apoc -e NEO4J_apoc_export_file_enabled=true -e NEO4J_apoc_import_file_enabled=true -e NEO4J_apoc_import_file_use__neo4j__config=true -e NEO4J_PLUGINS=\\[\\\"apoc-extended\\\"\\] neo4j\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "JKEORJfWwI7w"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "LLM_BACKEND = \"ollama\" # choose [\"ollama\" | \"lm-studio\"]\n",
+    "# LLM_BACKEND = \"lm-studio\"\n",
+    "\n",
+    "assert LLM_BACKEND in [\"ollama\", \"lm-studio\"]\n",
+    "\n",
+    "if LLM_BACKEND == \"ollama\":\n",
+    "  base_url = f\"http://{os.environ.get('OLLAMA_HOST', 'localhost')}:11434/v1\"\n",
+    "  api_key = \"ollama\"\n",
+    "  # llm_model = \"minicpm-v\"\n",
+    "  llm_model = \"phi4\"\n",
+    "else:\n",
+    "  base_url = \"http://localhost:1234/v1\"\n",
+    "  api_key = \"lm-studio\"\n",
+    "  llm_model = \"minicpm-llama3-v-2_5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FIEW2lBMqp2V"
+   },
+   "source": [
+    "If Colab you need to download ollama and start the server"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ProgressResponse(status='success', completed=None, total=None, digest=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ollama\n",
+    "\n",
+    "# ollama.pull(llm_model)\n",
+    "# ollama.pull(\"nomic-embed-text\")\n",
+    "ollama.pull(\"phi4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ListResponse(models=[Model(model='phi4:latest', modified_at=datetime.datetime(2025, 6, 21, 20, 30, 17, 738327, tzinfo=TzInfo(UTC)), digest='ac896e5b8b34a1f4efa7b14d7520725140d5512484457fab45d2a4ea14c69dba', size=9053116391, details=ModelDetails(parent_model='', format='gguf', family='phi3', families=['phi3'], parameter_size='14.7B', quantization_level='Q4_K_M'))])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ollama.list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bK17PDF7KxCv"
+   },
+   "source": [
+    "# Neo4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OUWxS0p0-d-e",
+    "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from neo4j import GraphDatabase\n",
+    "from langchain_neo4j import Neo4jGraph\n",
+    "\n",
+    "host = os.environ.get(\"NEO4J_HOST\", \"localhost\")\n",
+    "\n",
+    "# ---- Step 1: Setup Neo4j Connection ----\n",
+    "NEO4J_URI = f\"bolt://{host}:7687\"\n",
+    "NEO4J_USER = \"neo4j\"\n",
+    "NEO4J_PASSWORD = \"neo5j\"\n",
+    "\n",
+    "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))\n",
+    "graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)\n",
+    "\n",
+    "# ---- Step 2: Create knowledge graph from text ----\n",
+    "import os\n",
+    "from langchain_experimental.graph_transformers.llm import LLMGraphTransformer\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0,\n",
+    "                 model_name=llm_model,\n",
+    "                 base_url=base_url,\n",
+    "                 api_key=api_key)\n",
+    "\n",
+    "llm_transformer = LLMGraphTransformer(llm=llm)\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "text = \"\"\"\n",
+    "Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.\n",
+    "She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.\n",
+    "Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.\n",
+    "She was, in 1906, the first woman to become a professor at the University of Paris.\n",
+    "\"\"\"\n",
+    "documents = [Document(page_content=text)]\n",
+    "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n",
+    "print(f\"Nodes:{graph_documents[0].nodes}\")\n",
+    "print(f\"Relationships:{graph_documents[0].relationships}\")\n",
+    "\n",
+    "# Add graph to neo4j\n",
+    "graph.add_graph_documents(graph_documents)\n",
+    "\n",
+    "# ---- Step 3: Perform GraphRAG ----\n",
+    "\n",
+    "def escape(s):\n",
+    "  return s.replace(\"{\",\"\").replace(\"}\",\"\")\n",
+    "\n",
+    "CYPHER_GENERATION_TEMPLATE = f\"\"\"You are a Neo4j expert. Generate a Cypher query to answer the given question.\n",
+    "\n",
+    "Database Schema: {escape(graph.schema)}\n",
+    "\n",
+    "Rules:\n",
+    "1. Always use explicit `MATCH` for relationships.\n",
+    "2. Never use `WHERE` for relationship matching.\n",
+    "3. Use `RETURN DISTINCT` when appropriate.\n",
+    "\n",
+    "Example Queries:\n",
+    "1. Question: \"Who won the Nobel Prize?\"\n",
+    "   Cypher: MATCH (p:Person)-[:WON_NOBEL_PRIZE]->(:Awarded) RETURN p.id AS winner\n",
+    "\n",
+    "Question: {{query}}\n",
+    "Return only the Cypher query without any explanation or additional text.\n",
+    "Cypher:\"\"\"\n",
+    "\n",
+    "from langchain_neo4j import GraphCypherQAChain\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "chain = GraphCypherQAChain.from_llm(\n",
+    "    llm=llm,\n",
+    "    graph=graph,\n",
+    "    verbose=True,\n",
+    "    cypher_prompt=PromptTemplate(\n",
+    "        input_variables=[\"query\"],\n",
+    "        template=CYPHER_GENERATION_TEMPLATE\n",
+    "    ),\n",
+    "    allow_dangerous_requests=True\n",
+    ")\n",
+    "\n",
+    "# ---- Step 5: Test Queries ----\n",
+    "print(\"\\nTesting queries...\")\n",
+    "\n",
+    "question = \"Who married a Nobel Prize?\"\n",
+    "\n",
+    "print(f\"\\nQuestion: {question}\")\n",
+    "response = chain.invoke(question)\n",
+    "print(\"Response:\", response['result'])\n",
+    "\n",
+    "# Close the driver\n",
+    "driver.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A4gzGQzeeqFC"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "chap12",
+   "language": "python",
+   "name": "chap12"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Chapter12/poetry.lock b/Chapter12/poetry.lock
new file mode 100644
index 0000000..2485f78
--- /dev/null
+++ b/Chapter12/poetry.lock
@@ -0,0 +1,4277 @@
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+
+[[package]]
+name = "accelerate"
+version = "1.8.1"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
+    {file = "accelerate-1.8.1-py3-none-any.whl", hash = "sha256:c47b8994498875a2b1286e945bd4d20e476956056c7941d512334f4eb44ff991"},
+    {file = "accelerate-1.8.1.tar.gz", hash = "sha256:f60df931671bc4e75077b852990469d4991ce8bd3a58e72375c3c95132034db9"},
+]
+
+[package.dependencies]
+huggingface_hub = ">=0.21.0"
+numpy = ">=1.17,<3.0.0"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.4.3"
+torch = ">=2.0.0"
+
+[package.extras]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.11.2,<0.12.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,<0.12.0)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-fp8 = ["torchao"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+
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+    {file = "zstandard-0.23.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:11e3bf3c924853a2d5835b24f03eeba7fc9b07d8ca499e247e06ff5676461a15"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2fb4535137de7e244c230e24f9d1ec194f61721c86ebea04e1581d9d06ea1269"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8c24f21fa2af4bb9f2c492a86fe0c34e6d2c63812a839590edaf177b7398f700"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:a8c86881813a78a6f4508ef9daf9d4995b8ac2d147dcb1a450448941398091c9"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:fe3b385d996ee0822fd46528d9f0443b880d4d05528fd26a9119a54ec3f91c69"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:82d17e94d735c99621bf8ebf9995f870a6b3e6d14543b99e201ae046dfe7de70"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:c7c517d74bea1a6afd39aa612fa025e6b8011982a0897768a2f7c8ab4ebb78a2"},
+    {file = "zstandard-0.23.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:1fd7e0f1cfb70eb2f95a19b472ee7ad6d9a0a992ec0ae53286870c104ca939e5"},
+    {file = "zstandard-0.23.0-cp39-cp39-win32.whl", hash = "sha256:43da0f0092281bf501f9c5f6f3b4c975a8a0ea82de49ba3f7100e64d422a1274"},
+    {file = "zstandard-0.23.0-cp39-cp39-win_amd64.whl", hash = "sha256:f8346bfa098532bc1fb6c7ef06783e969d87a99dd1d2a5a18a892c1d7a643c58"},
+    {file = "zstandard-0.23.0.tar.gz", hash = "sha256:b2d8c62d08e7255f68f7a740bae85b3c9b8e5466baa9cbf7f57f1cde0ac6bc09"},
+]
+
+[package.dependencies]
+cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
+
+[package.extras]
+cffi = ["cffi (>=1.11)"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = "~3.10"
+content-hash = "04a1e405076c739a06f42905818b2b4ddfb84ea4c125b4fc1367ce4f2a7e8962"
diff --git a/Chapter12/pyproject.toml b/Chapter12/pyproject.toml
new file mode 100644
index 0000000..dc5bcc3
--- /dev/null
+++ b/Chapter12/pyproject.toml
@@ -0,0 +1,35 @@
+[tool.poetry]
+name = "Graph Machine Learning (2nd Edition) - Chapter 13"
+version = "1.0.0"
+description = ""
+authors = ["Enrico Deusebio <edeusebio85@gmail.com>"]
+packages = []
+package-mode = false
+
+[tool.setuptools]
+py-modules = []
+
+[tool.poetry.dependencies]
+python = "~3.10"
+ipykernel = ">=6.0.0" 
+matplotlib = "==3.2.2"
+numpy = ">=1.26,<2.0"
+# networkx = "==2.5"
+torch = "^2.1.0"
+torch_geometric = "^2.5.2"
+torchvision = "^0.16.0"
+torchmetrics="^1.3.0"
+torch-sparse = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp310-cp310-linux_x86_64.whl"}
+torch-scatter = {url = "https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp310-cp310-linux_x86_64.whl"}
+neo4j-graphrag = {extras = ["ollama"], version="^1.6.1"}
+langchain-community = "^0.3.21"
+langchain-experimental = "^0.3.4"
+langchain-neo4j = "^0.4.0"
+langchain-openai = "^0.3.12"
+transformers = {extras = ["torch"], version = "^4.50.3"}
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
diff --git a/Chapter12/requirements.txt b/Chapter12/requirements.txt
new file mode 100644
index 0000000..5db35d6
--- /dev/null
+++ b/Chapter12/requirements.txt
@@ -0,0 +1,137 @@
+accelerate==1.8.1 ; python_version == "3.10"
+aiohappyeyeballs==2.6.1 ; python_version == "3.10"
+aiohttp==3.12.13 ; python_version == "3.10"
+aiosignal==1.3.2 ; python_version == "3.10"
+annotated-types==0.7.0 ; python_version == "3.10"
+anyio==4.9.0 ; python_version == "3.10"
+appnope==0.1.4 ; platform_system == "Darwin" and python_version == "3.10"
+asttokens==3.0.0 ; python_version == "3.10"
+async-timeout==4.0.3 ; python_version == "3.10"
+attrs==25.3.0 ; python_version == "3.10"
+certifi==2025.6.15 ; python_version == "3.10"
+cffi==1.17.1 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10"
+charset-normalizer==3.4.2 ; python_version == "3.10"
+colorama==0.4.6 ; python_version == "3.10" and (sys_platform == "win32" or platform_system == "Windows")
+comm==0.2.2 ; python_version == "3.10"
+cycler==0.12.1 ; python_version == "3.10"
+dataclasses-json==0.6.7 ; python_version == "3.10"
+debugpy==1.8.14 ; python_version == "3.10"
+decorator==5.2.1 ; python_version == "3.10"
+distro==1.9.0 ; python_version == "3.10"
+exceptiongroup==1.3.0 ; python_version == "3.10"
+executing==2.2.0 ; python_version == "3.10"
+filelock==3.18.0 ; python_version == "3.10"
+frozenlist==1.7.0 ; python_version == "3.10"
+fsspec==2024.12.0 ; python_version == "3.10"
+greenlet==3.2.3 ; python_version == "3.10" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
+h11==0.16.0 ; python_version == "3.10"
+hf-xet==1.1.5 ; (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64") and python_version == "3.10"
+httpcore==1.0.9 ; python_version == "3.10"
+httpx-sse==0.4.0 ; python_version == "3.10"
+httpx==0.28.1 ; python_version == "3.10"
+huggingface-hub==0.33.0 ; python_version == "3.10"
+idna==3.10 ; python_version == "3.10"
+ipykernel==6.29.5 ; python_version == "3.10"
+ipython==8.37.0 ; python_version == "3.10"
+jedi==0.19.2 ; python_version == "3.10"
+jinja2==3.1.6 ; python_version == "3.10"
+jiter==0.10.0 ; python_version == "3.10"
+json-repair==0.39.1 ; python_version == "3.10"
+jsonpatch==1.33 ; python_version == "3.10"
+jsonpointer==3.0.0 ; python_version == "3.10"
+jupyter-client==8.6.3 ; python_version == "3.10"
+jupyter-core==5.8.1 ; python_version == "3.10"
+kiwisolver==1.4.8 ; python_version == "3.10"
+langchain-community==0.3.26 ; python_version == "3.10"
+langchain-core==0.3.66 ; python_version == "3.10"
+langchain-experimental==0.3.4 ; python_version == "3.10"
+langchain-neo4j==0.4.0 ; python_version == "3.10"
+langchain-openai==0.3.24 ; python_version == "3.10"
+langchain-text-splitters==0.3.8 ; python_version == "3.10"
+langchain==0.3.26 ; python_version == "3.10"
+langsmith==0.4.1 ; python_version == "3.10"
+lightning-utilities==0.14.3 ; python_version == "3.10"
+markupsafe==3.0.2 ; python_version == "3.10"
+marshmallow==3.26.1 ; python_version == "3.10"
+matplotlib-inline==0.1.7 ; python_version == "3.10"
+matplotlib==3.2.2 ; python_version == "3.10"
+mpmath==1.3.0 ; python_version == "3.10"
+multidict==6.5.0 ; python_version == "3.10"
+mypy-extensions==1.1.0 ; python_version == "3.10"
+neo4j-graphrag==1.7.0 ; python_version == "3.10"
+neo4j-graphrag[ollama]==1.7.0 ; python_version == "3.10"
+neo4j==5.28.1 ; python_version == "3.10"
+nest-asyncio==1.6.0 ; python_version == "3.10"
+networkx==3.4.2 ; python_version == "3.10"
+numpy==1.26.4 ; python_version == "3.10"
+nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-nvjitlink-cu12==12.9.86 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+ollama==0.4.9 ; python_version == "3.10"
+openai==1.90.0 ; python_version == "3.10"
+orjson==3.10.18 ; platform_python_implementation != "PyPy" and python_version == "3.10"
+packaging==24.2 ; python_version == "3.10"
+parso==0.8.4 ; python_version == "3.10"
+pexpect==4.9.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10"
+pillow==11.2.1 ; python_version == "3.10"
+platformdirs==4.3.8 ; python_version == "3.10"
+prompt-toolkit==3.0.51 ; python_version == "3.10"
+propcache==0.3.2 ; python_version == "3.10"
+psutil==7.0.0 ; python_version == "3.10"
+ptyprocess==0.7.0 ; sys_platform != "win32" and sys_platform != "emscripten" and python_version == "3.10"
+pure-eval==0.2.3 ; python_version == "3.10"
+pycparser==2.22 ; (implementation_name == "pypy" or platform_python_implementation == "PyPy") and python_version == "3.10"
+pydantic-core==2.33.2 ; python_version == "3.10"
+pydantic-settings==2.9.1 ; python_version == "3.10"
+pydantic==2.11.7 ; python_version == "3.10"
+pygments==2.19.1 ; python_version == "3.10"
+pyparsing==3.2.3 ; python_version == "3.10"
+pypdf==5.6.0 ; python_version == "3.10"
+python-dateutil==2.9.0.post0 ; python_version == "3.10"
+python-dotenv==1.1.0 ; python_version == "3.10"
+pytz==2025.2 ; python_version == "3.10"
+pywin32==310 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version == "3.10"
+pyyaml==6.0.2 ; python_version == "3.10"
+pyzmq==27.0.0 ; python_version == "3.10"
+regex==2024.11.6 ; python_version == "3.10"
+requests-toolbelt==1.0.0 ; python_version == "3.10"
+requests==2.32.4 ; python_version == "3.10"
+safetensors==0.5.3 ; python_version == "3.10"
+scipy==1.15.3 ; python_version == "3.10"
+setuptools==80.9.0 ; python_version == "3.10"
+six==1.17.0 ; python_version == "3.10"
+sniffio==1.3.1 ; python_version == "3.10"
+sqlalchemy==2.0.41 ; python_version == "3.10"
+stack-data==0.6.3 ; python_version == "3.10"
+sympy==1.14.0 ; python_version == "3.10"
+tenacity==9.1.2 ; python_version == "3.10"
+tiktoken==0.9.0 ; python_version == "3.10"
+tokenizers==0.21.1 ; python_version == "3.10"
+torch-geometric==2.6.1 ; python_version == "3.10"
+torch-scatter @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_scatter-2.1.2%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch-sparse @ https://data.pyg.org/whl/torch-2.1.0%2Bcpu/torch_sparse-0.6.18%2Bpt21cpu-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch==2.1.2 ; python_version == "3.10"
+torchmetrics==1.7.3 ; python_version == "3.10"
+torchvision==0.16.2 ; python_version == "3.10"
+tornado==6.5.1 ; python_version == "3.10"
+tqdm==4.67.1 ; python_version == "3.10"
+traitlets==5.14.3 ; python_version == "3.10"
+transformers[torch]==4.52.4 ; python_version == "3.10"
+triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
+types-pyyaml==6.0.12.20250516 ; python_version == "3.10"
+typing-extensions==4.14.0 ; python_version == "3.10"
+typing-inspect==0.9.0 ; python_version == "3.10"
+typing-inspection==0.4.1 ; python_version == "3.10"
+urllib3==2.5.0 ; python_version == "3.10"
+wcwidth==0.2.13 ; python_version == "3.10"
+yarl==1.20.1 ; python_version == "3.10"
+zstandard==0.23.0 ; python_version == "3.10"
diff --git a/Chapter12/setup_ollama.sh b/Chapter12/setup_ollama.sh
new file mode 100755
index 0000000..1571c39
--- /dev/null
+++ b/Chapter12/setup_ollama.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+curl -fsSL https://ollama.com/install.sh | sudo sh
+
+ollama -v
+
+ollama pull minicpm-v
+ollama pull nomic-embed-text
+
diff --git a/README.md b/README.md
index d2c504e..a62a9fe 100644
--- a/README.md
+++ b/README.md
@@ -26,10 +26,6 @@ If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1800
 <a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" 
 alt="https://www.packtpub.com/" border="5" /></a>
 
-## Errata
-Page 16
-
-The expression nt.to.numpy.matrix(G) should be nx.to.numpy.matrix(G)
 
 ## Instructions and Navigations
 All of the code is organized into folders. For example, Chapter02.
@@ -44,6 +40,15 @@ generator = HinSAGENodeGenerator(
  head_node_type="document"
 )
 ```
+
+The notebooks in the repositories save files and figures into dedicated folders as they are executed. The format for these folders are:
+
+```
+<DATA_FOLDER>/Chapter<n>
+```
+
+where `DATA_FOLDER` is an environment variable that you can use to customize the position for the data. If the variable is not set, its value fall back to `<root_folder_repo>/data`.
+
 **Following is what you need for this book:**
 This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance. The book will also be useful for data scientists and machine learning developers who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.
 
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..14655ee
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,94 @@
+FROM jupyter/scipy-notebook as base
+
+ARG user=euler
+ARG branch=main
+
+USER root
+
+RUN apt-get update && apt-get install git-all -y
+
+RUN useradd -ms /bin/bash ${user}
+RUN deluser --remove-home jovyan
+
+USER ${user}
+
+ENV HOME /home/${user}
+ENV NB_USER=${user}
+ENV XDG_CACHE_HOME=/home/${user}/.cache/
+ENV DATA_FOLDER=/data
+
+RUN git clone https://github.com/deusebio/Graph-Machine-Learning.git /home/${user}/Graph-Machine-Learning
+WORKDIR /home/${user}/Graph-Machine-Learning
+RUN git checkout ${branch}
+
+RUN ln -s /data data
+EXPOSE 8888
+
+ENTRYPOINT jupyter notebook --no-browser --port 8888 --NotebookApp.token='' --NotebookApp.password=''
+
+FROM base as chap1
+RUN ls -d -1 */  | grep -v -e Chapter01 | xargs rm -rf
+RUN conda create -n chap1 python=3.9
+RUN conda run -n chap1 pip install -r Chapter01/requirements.txt
+RUN conda run -n chap1 python -m ipykernel install --name chap1 --user
+
+FROM base as chap2
+RUN ls -d -1 */  | grep -v -e Chapter02 | xargs rm -rf
+RUN conda create -n chap2 python=3.11
+RUN conda run -n chap2 pip install -r Chapter02/requirements.txt
+RUN conda run -n chap2 python -m ipykernel install --name chap2 --user
+
+FROM base as chap3
+RUN ls -d -1 */  | grep -v -e Chapter03 | xargs rm -rf
+RUN conda create -n chap3 python=3.8
+RUN conda run -n chap3 pip install -r Chapter03/requirements.txt
+RUN conda run -n chap3 python -m ipykernel install --name chap3 --user
+
+FROM base as chap4
+RUN ls -d -1 */  | grep -v -e Chapter04 | xargs rm -rf
+RUN conda create -n chap4 python=3.8
+RUN conda run -n chap4 pip install -r Chapter04/requirements.txt
+RUN conda run -n chap4 python -m ipykernel install --name chap4 --user
+
+FROM base as chap5
+RUN ls -d -1 */  | grep -v -e Chapter05 | xargs rm -rf
+RUN conda create -n chap5 python=3.8
+RUN conda run -n chap5 pip install -r Chapter05/requirements.txt
+RUN conda run -n chap5 python -m ipykernel install --name chap5 --user
+
+FROM base as chap6
+RUN ls -d -1 */  | grep -v -e Chapter06 | xargs rm -rf
+RUN conda create -n chap6 python=3.8
+RUN conda run -n chap6 pip install -r Chapter06/requirements.txt
+RUN conda run -n chap6 python -m ipykernel install --name chap6 --user
+
+FROM base as chap7
+RUN ls -d -1 */  | grep -v -e Chapter07 | xargs rm -rf
+RUN conda create -n chap7 python=3.8
+RUN conda run -n chap7 pip install -r Chapter07/requirements.txt
+RUN conda run -n chap7 python -m ipykernel install --name chap7 --user
+
+FROM base as chap8
+RUN ls -d -1 */  | grep -v -e Chapter08 | xargs rm -rf
+RUN conda create -n chap8 python=3.8
+RUN conda run -n chap8 pip install -r Chapter08/requirements.txt
+RUN conda run -n chap8 python -m ipykernel install --name chap8 --user
+
+FROM base as chap9
+RUN ls -d -1 */  | grep -v -e Chapter09 | xargs rm -rf
+RUN conda create -n chap9 python=3.8
+RUN conda run -n chap9 pip install -r Chapter09/requirements.txt
+RUN conda run -n chap9 python -m ipykernel install --name chap9 --user
+
+FROM base as chap10
+RUN ls -d -1 */  | grep -v -e Chapter10 | xargs rm -rf
+RUN conda create -n chap10 python=3.10
+RUN conda run -n chap10 pip install -r Chapter10/requirements.txt
+RUN conda run -n chap10 python -m ipykernel install --name chap10 --user
+
+FROM base as chap12
+RUN ls -d -1 */  | grep -v -e Chapter12 | xargs rm -rf
+RUN conda create -n chap12 python=3.10
+RUN conda run -n chap12 pip install -r Chapter12/requirements.txt
+RUN conda run -n chap12 python -m ipykernel install --name chap12 --user
+# RUN /bin/bash ./Chapter12/setup_ollama.sh
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 0000000..cfd50d5
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,88 @@
+# Docker image
+
+In order to ensure reproducibility and an environment ready to be used to test the examples from the different chapters, we provide a Docker image with several Python environments already installed, corresponding to the dependencies set of the different chapters.
+
+The dependencies sets of the different chapters are handled using [Poetry](https://python-poetry.org/) to both provide an easy way to manage dependencies updates as well as produce pinned `requirements.txt` files representing the entire environment. In fact, in the first version of the book, we realized that transitive dependendencies of some of the packages we used, when not explicitely pinned, could break the installation process.
+
+## Usage
+
+### Build the images
+
+To build the image from this local directory, run the following command
+
+```bash
+$ docker build . -t graph-machine-learning:latest --no-cache
+```
+
+### Run the image
+
+We generally recommend to create a local directory where to store data, results and images
+
+```bash 
+$ mkdir data
+```
+
+Then, use the following command
+
+```bash
+$ docker run --rm \
+  	 -p <port>:8888 \
+	 -v "$(pwd)/data:/data"  \
+	 --name graph-machine-learning-box \
+	 graph-machine-learning:latest
+```
+
+to start the image. Please make sure that the data folder can be written by the Docker image. We suggest to use the default port 8888 for the `<port>`. This will start a Jupyter server which should be locally accessible at `[http://localhost:8888](http://localhost:8888)` (or change the port accordingly). 
+
+## For Developers
+
+Make sure that in your system, [Poetry]((https://python-poetry.org/) is correctly installed and configured, use the following command to verify this
+
+```bash
+$ poetry --version
+```
+
+### Update Dependencies
+
+Dependencies may need to be updated, when:
+
+* **Adding new packages**
+New dependencies can be added directly using Poetry with the following command
+```bash
+$ poetry add <package>
+```
+This should modify the `pyproject.toml` file with the new dependency. 
+
+* **Update dependent packages to new release**
+```bash
+$ poetry update
+```
+
+Both these action will create a new `poetry.lock` file. 
+
+Once the `poetry.lock` file is updated, we can then export a new `requirements.txt` file using
+
+```bash
+$ poetry export -f requirements.txt --output requirements.txt --without-hashes
+```
+
+### Testing
+
+In order to make sure that the docker image is fully working, we provide a Bash script to run through all the notebooks of the image, and provide a summary of successful/failing notebooks.
+
+Before running the tests, make sure the image is running with the `graph-machine-learning-box` name attached to it (see section *Usage* above).
+
+To run the tests, use
+
+```bash 
+./tests.sh
+```
+
+In order to run tests only for particular chapters, provide the name of the chapters as extra arguments, e.g. 
+
+```bash 
+./tests.sh Chapter01 Chapter02
+```
+
+Needless to say, we expect all of the notebooks to properly run.
+
diff --git a/docker/tests.sh b/docker/tests.sh
new file mode 100755
index 0000000..820cd67
--- /dev/null
+++ b/docker/tests.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+
+# docker run --rm --name graph-machine-learning-box graph-machine-learning:latest
+
+LOG_PREFIX="[ImageIntegrationTests]"
+
+CMD="docker exec graph-machine-learning-box"
+
+ARGS=$*
+
+FILES=$(${CMD} find $* -maxdepth 2 -name "*.ipynb" | sort)
+
+SUCCESS=0
+FAILURE=0
+SKIP=0
+
+ERRORS=""
+
+for _FILE in $FILES; do
+    case $_FILE in
+
+	./Chapter0[4,5,6,7,8,9]*)
+	    echo "${LOG_PREFIX} Skipping file ${_FILE}"
+	    let SKIP=SKIP+1
+	    ;;
+	*)
+	    echo "${LOG_PREFIX} Testing ${_FILE}"
+	    OUT=$(${CMD} jupyter nbconvert --execute --to notebook $_FILE --output /tmp/tmp.ipynb)
+	    if [[ $? == "0" ]]; then
+		let SUCCESS=SUCCESS+1
+	    else
+		let FAILURE=FAILURE+1
+		ERRORS="${ERRORS}\n====== ${_FILE} ======\n ${OUT}\n================="
+	    fi
+	    ;;
+    esac
+done
+
+rm -rf /tmp/tmp.ipynb
+
+let TOTAL=SUCCESS+FAILURE+SKIP
+
+echo "${LOG_PREFIX} "
+echo "${LOG_PREFIX} ************************************* "
+echo "${LOG_PREFIX} Summary"
+echo "${LOG_PREFIX} ************************************* "
+echo "${LOG_PREFIX} "
+echo "${LOG_PREFIX} TOTAL: ${TOTAL}"
+echo "${LOG_PREFIX} "
+echo "${LOG_PREFIX} SUCCESS: ${SUCCESS}"
+echo "${LOG_PREFIX} FAILURE: ${FAILURE}"
+echo "${LOG_PREFIX} SKIP: ${SKIP}"
+echo "${LOG_PREFIX} ************************************* "
+
+# Provide 1 exit code if any failure has happened
+if [[ "${FAILURE}" != "0" ]];
+then
+    exit 1
+else
+    exit 0
+fi
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..a524daf
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,73 @@
+import os
+
+import networkx as nx
+import pathlib
+import matplotlib.pyplot as plt
+
+_chapter = os.path.basename(os.getcwd())
+
+if _chapter.startswith("Chapter"):
+    BASE_FOLDER = os.environ.get("DATA_FOLDER", os.path.join(os.getcwd(), "..", "data"))
+    DATA_DIR = pathlib.Path(BASE_FOLDER) / _chapter
+else:
+    BASE_FOLDER = os.environ.get("DATA_FOLDER", os.getcwd())
+    DATA_DIR = pathlib.Path(BASE_FOLDER)
+
+FIGURES_DIR = DATA_DIR / "figures"
+
+default_edge_color = 'gray'
+default_node_color = '#407cc9'
+enhanced_node_color = '#f5b042'
+enhanced_edge_color = '#cc2f04'
+
+if not FIGURES_DIR.exists():
+    FIGURES_DIR.mkdir(parents=True)
+
+# draw a simple graph
+def draw_graph(G, node_names={}, filename=None, node_size=50, layout = None, plot_weight=False):
+    pos_nodes = nx.spring_layout(G) if layout is None else layout(G)
+    node_names = {k: k for k, v in G.nodes.items()} if not node_names else node_names
+    nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')
+    
+    pos_attrs = {}
+    for node, coords in pos_nodes.items():
+        pos_attrs[node] = (coords[0], coords[1] + 0.08)
+    
+    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif', font_size=20)
+
+    if plot_weight:
+        edge_labels=dict([((a,b,),d["weight"]) for a,b,d in G.edges(data=True)])
+        nx.draw_networkx_edge_labels(G, pos_nodes, edge_labels=edge_labels)
+    
+    plt.axis('off')
+    axis = plt.gca()
+    axis.set_xlim([1.2*x for x in axis.get_xlim()])
+    axis.set_ylim([1.2*y for y in axis.get_ylim()])
+    
+    if filename:
+        plt.savefig(FIGURES_DIR / filename, format="png")
+
+
+# draw enhanced path on the graph
+def draw_enhanced_path(G, path_to_enhance, node_names={}, filename=None, layout = None):
+    path_edges = list(zip(path_to_enhance,path_to_enhance[1:]))
+    pos_nodes = nx.spring_layout(G) if layout is None else layout(G)
+
+    plt.figure(figsize=(5,5),dpi=300)
+    pos_nodes = nx.spring_layout(G)
+    nx.draw(G, pos_nodes, with_labels=False, node_size=50, edge_color='gray')
+    
+    pos_attrs = {}
+    for node, coords in pos_nodes.items():
+        pos_attrs[node] = (coords[0], coords[1] + 0.08)
+        
+    nx.draw_networkx_labels(G, pos_attrs, labels=node_names, font_family='serif')
+    nx.draw_networkx_edges(G,pos_nodes,edgelist=path_edges, edge_color='#cc2f04', style='dashed', width=2.0)
+    
+    plt.axis('off')
+    axis = plt.gca()
+    axis.set_xlim([1.2*x for x in axis.get_xlim()])
+    axis.set_ylim([1.2*y for y in axis.get_ylim()])
+    
+    if filename:
+        plt.savefig(FIGURES_DIR / filename, format="png")