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Social Network Analysis

Facebook Social Network Visualization

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