Social Network Analysis

Overview

This project explores various types of social network structures using NetworkX and Matplotlib. It consists of three main components:

1. Clustered Social Network: Generates a social network with multiple clusters, where intra-cluster connections are stronger than inter-cluster connections.
2. Random Graph Rewiring: Demonstrates how edges in a network can be rewired with a given probability to study the effects of randomness on structure.
3. Random Degree Distribution Network: Generates a graph with a given degree distribution and allows for rewiring to simulate different network dynamics.

Dependencies

Ensure you have the following Python libraries installed:

pip install networkx matplotlib numpy

1. Clustered Social Network

This script generates a social network with distinct clusters. Nodes within a cluster have a higher chance of being connected than nodes between clusters. The method used is similar to the Girvan-Newman structure.

Output

• A visualization of a clustered social network.

2. Random Graph Rewiring

This script starts with a randomly generated Erdős–Rényi graph and rewires its edges based on a given probability. This demonstrates how a network can transition from structured to more randomized connectivity.

Output

• Four different rewired versions of the original graph displayed in a 2x2 subplot.

3. Random Degree Distribution Network

This script generates a network where nodes follow a predefined degree distribution using the Configuration Model. The sum of degrees is adjusted to ensure a valid network.

Output

• A network visualization based on the generated degree sequence.
• Four rewired versions of this graph to illustrate changes in topology.

Visualization Results

• The images produced by each of the three scripts are saved and can be used for analysis and presentation.

Usage

Run each script separately to generate and visualize the respective networks. Modify parameters such as p_in, p_out, and p to observe different behaviors in network structures.

Authors & Contributions

• Developer: Ali Alahyrlou
• Visualization: Generated using Matplotlib and NetworkX