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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", + "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." + ], + "metadata": { + "id": "BIK1pgU4_lWd" + } + }, + { + "cell_type": "code", + "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}\")" + ], + "metadata": { + "id": "2-3ku6QR_ZWa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 701, + "referenced_widgets": [ + "947a2ce0171c4582843c5afb590471fb", + "f43faf84d7f041b08bcbec1aeeb5297a", + "d3fda1a8024c4f308dc36d2216006a46", + "ebd67e8c91f94cda8c314ff8c387a7d4", + "4a1994565cd541389bacb618bfb889cc", + "9b6d7a51282e46409eedb05fa786e629", + "0a0a68b09ba54987b47fd441d23c8870", + "28ce9bcc08504d7eb458d15b955005a9", + "a17cd8706d5a4961858297899f996a86", + "93995324744e414b8b88453ea975245f", + "66779461ba7f42a796d5cced4dbde20d", + "fb23e41eee404e1681c922bfda1f0c68", + "1dbae508a6c44c8380c809c5acb13ddd", + "d57001a6ba65463fb4e2bd4621f34f33", + "d66f73a1b19540448d470371576bca83", + 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Chapter06/03_graph_similarity.ipynb | 8162 ++++++++++++++------------- Chapter06/poetry.lock | 2769 ++++++--- Chapter06/pyproject.toml | 10 +- Chapter06/requirements.txt | 237 +- 4 files changed, 6288 insertions(+), 4890 deletions(-) diff --git a/Chapter06/03_graph_similarity.ipynb b/Chapter06/03_graph_similarity.ipynb index e579160..d8ebe9e 100644 --- a/Chapter06/03_graph_similarity.ipynb +++ b/Chapter06/03_graph_similarity.ipynb @@ -1,4105 +1,4115 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { "colab": { - "provenance": [] + "base_uri": "https://localhost:8080/", + "height": 701, + "referenced_widgets": [ + "947a2ce0171c4582843c5afb590471fb", + "f43faf84d7f041b08bcbec1aeeb5297a", + "d3fda1a8024c4f308dc36d2216006a46", + "ebd67e8c91f94cda8c314ff8c387a7d4", + "4a1994565cd541389bacb618bfb889cc", + "9b6d7a51282e46409eedb05fa786e629", + "0a0a68b09ba54987b47fd441d23c8870", + "28ce9bcc08504d7eb458d15b955005a9", + "a17cd8706d5a4961858297899f996a86", + "93995324744e414b8b88453ea975245f", + "66779461ba7f42a796d5cced4dbde20d", + "fb23e41eee404e1681c922bfda1f0c68", + "1dbae508a6c44c8380c809c5acb13ddd", + "d57001a6ba65463fb4e2bd4621f34f33", + "d66f73a1b19540448d470371576bca83", + "51c89c0f6e0e47d18921a58d801b2f63", + "75d57c5cbf274d0ba193c3174436ba59", + "9b8b3255b71343c9bdc4c9bbf1fec334", + 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+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" } -# This is what is holding us back to python 3.8 stellargraph = { git="https://github.com/stellargraph/stellargraph.git", branch="develop" } [build-system] diff --git a/Chapter06/requirements.txt b/Chapter06/requirements.txt index 797e6cf..3e1f9f7 100644 --- a/Chapter06/requirements.txt +++ b/Chapter06/requirements.txt @@ -1,101 +1,136 @@ -absl-py==2.1.0 ; 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" -backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.9" -cachetools==5.4.0 ; python_version >= "3.8" and python_version < "3.9" -certifi==2024.7.4 ; python_version >= "3.8" and python_version < "3.9" -cffi==1.17.0 ; 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" -communities==2.2.0 ; 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==1.8.5 ; 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==2.0.7 ; 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==2.33.0 ; python_version >= "3.8" and python_version < "3.9" -google-pasta==0.2.0 ; python_version >= "3.8" and python_version < "3.9" -grpcio==1.65.4 ; python_version >= "3.8" and python_version < "3.9" -h5py==3.11.0 ; python_version >= "3.8" and python_version < "3.9" -idna==3.7 ; python_version >= "3.8" and python_version < "3.9" -importlib-metadata==8.2.0 ; python_version >= "3.8" and python_version < "3.9" -ipykernel==6.29.5 ; python_version >= "3.8" and python_version < "3.9" 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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" -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==3.3.0 ; 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" -platformdirs==4.2.2 ; 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