This project implements high-performance graph algorithms in Rust to analyze real-world social network structures. By processing Facebook ego graph data, it identifies key influencers, measures network connectivity, and uncovers community clusters.
- Path Analysis: Utilizes Breadth-First Search (BFS) to calculate average path distances across the network.
- Influence Mapping: Computes Closeness Centrality to pinpoint the most "central" or influential nodes.
- Similarity Scoring: Implements Jaccard Similarity based on mutual friends to identify closely linked user groups.
- Rust-Powered: Built entirely with Rust for safety and efficiency when handling large-scale graph datasets.
- View Documentation: For a deep dive into the methodology, refer to the Project Documentation.pdf.
- Explore the Code: The core logic is located in the facebook_graph_analysis directory.
- License: This project is licensed under the MIT License.