Amazon Co-Purchasing Network Analysis

This project explores the Amazon product co-purchasing network using graph theory. By applying Tarjan's algorithm to detect communities and measuring Jaccard similarity, I analyzed how products are interconnected through customer purchasing behavior.

    • Tarjan’s Strongly Connected Components (SCC)

    • Identified product communities within the network

    • Used depth-first search and stack structure via Rust’s petgraph crate

    • Jaccard Similarity

    • Measured product similarity based on common neighbors

    • Helped determine closely related products in each community

    • Common Customer Count

    • Analyzed how often product pairs shared buyers

    • Sorted product pairs by shared customer frequency

    • Language: Rust

    • Used petgraph, HashMap, and custom graph functions

    • Focused on performance and memory efficiency