Welcome to the Social Network Analysis repository! This project involves analyzing social network data to uncover key insights and patterns. It features various techniques for network exploration, centrality measurement, community detection, and recommendation system design.
This repository provides a comprehensive analysis of social network data, including:
- Data Exploration and Preprocessing: Understanding and preparing the dataset.
- Graph Construction: Building and analyzing network graphs.
- Community Detection: Identifying and analyzing communities within the network.
- Influencer Identification: Measuring centrality to find key influencers.
- Recommendation System: Designing algorithms to suggest new connections.
- Data Exploration and Preprocessing
- Graph Construction
- Community Detection
- Influencer Identification
- Recommendation System
The dataset used in this project is derived from the SNAP Facebook Social Circles dataset. It includes the following files:
- facebook_combined.txt: A combined file containing edges between users in the Facebook social network. Each line represents a connection between two users.
- facebook: The Facebook Social Circles dataset typically includes several files for each ego network.
- Download from : https://snap.stanford.edu/data/ego-Facebook.html
Open the Jupyter notebook in the notebooks/ directory for detailed analysis and implementation:
jupyter notebook notebooks/SocialNetworkAnalysis.ipynbIn the notebook, you’ll find:
- Data Exploration: Loading and inspecting the dataset.
- Graph Construction: Building and analyzing the network graph.
- Community Detection: Applying algorithms to identify communities.
- Influencer Identification: Calculating centrality measures.
- Recommendation System: Designing and evaluating connection recommendations.
For any questions or feedback, please contact:
- Your Name: (mailto:[email protected])
- GitHub: (https://github.com/Ananth09/)