Social Network Analysis

This project is an analysis of a social network using various network analysis techniques and visualization methods. It focuses on examining the Facebook social network to understand its structure, properties, and community dynamics.

Features

• Network Visualization: Generate visual representations of the social network.
• Adjacency Matrix Heatmap: Visualize the adjacency matrix of the network.
• Degree Distribution Analysis: Analyze and visualize the degree distribution of the network.
• Clustering Coefficient Analysis: Compute and visualize the clustering coefficient distribution.
• Centrality Measures: Calculate and visualize various centrality measures including eigenvalue centrality, closeness centrality, betweenness centrality, and degree centrality.
• Community Detection: Detect and visualize communities within the network using the Louvain method.
• Information Diffusion Models: Simulate information diffusion using the Linear Threshold (LT) and Independent Cascade (IC) models.

Technologies Used

• NetworkX: A Python library for the creation, manipulation, and study of complex networks.
• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
• NumPy: A library for the Python programming language, adding support for large, multi-dimensional arrays and matrices.

Installation

1. Clone this repository to your local machine:

   git clone https://github.com/SamaRostami/Social-Network-Analysis.git

2. Change into the project directory:

   cd social-network-analysis

3. Install necessary Python packages:

   pip install networkx matplotlib numpy

4. Download the dataset: Ensure you have the facebook_combined.txt.gz file in the project directory.

Usage

1. Run Analysis Scripts: Execute the provided scripts to perform various analyses on the social network dataset.
2. Generate Visualizations: Use the scripts to create visual representations and plots to help understand the network's structure.
3. Explore Centrality Measures: Examine the centrality measures to identify key nodes within the network.
4. Community Detection: Identify and visualize communities to see how the network is divided into subgroups.
5. Simulate Information Spread: Use the simulation models to study how information or influence spreads through the network.

License

This project is licensed under the MIT License.

Collaborators

• Samasky Rostami: You can contact me at samasky.rostami@gmail.com.
• Navid Mafi: You can find Navid's contributions and projects on GitHub at Navid Mafi's GitHub.