Friend Recommendation System

Project Overview

A sophisticated friend recommendation system that leverages graph theory and network analysis to suggest meaningful connections in a social network. Built with Flask and NetworkX, the system analyzes user relationships and identifies potential friends based on mutual connections and network proximity.

Key Features

• Graph-Based Analysis: Utilizes NetworkX to model social networks as graphs with users as nodes and friendships as edges
• Intelligent Recommendations: Analyzes neighbors and second-degree connections to suggest relevant friend candidates
• Multiple Algorithms: Implements various recommendation strategies including mutual friends, common interests, and network centrality
• Interactive Visualization: Frontend interface displaying network graphs and recommendation results
• RESTful API: Clean API endpoints for network operations and recommendation queries
• Scalable Architecture: Efficient graph algorithms optimized for large social networks

Technical Implementation

The system uses NetworkX's powerful graph algorithms to analyze social network structures. Flask provides the API layer, handling requests and coordinating between the graph analysis engine and the frontend. The recommendation algorithm considers factors like mutual friends, network distance, and clustering coefficients.

Challenges & Solutions

Challenge: Generating relevant recommendations without overwhelming users with too many suggestions.
Solution: Implemented a scoring system that ranks potential friends based on multiple factors and returns only the top N most relevant suggestions.

Challenge: Optimizing performance for large social networks.
Solution: Used NetworkX's optimized graph algorithms and implemented caching for frequently accessed network metrics.

Impact & Results

This project demonstrates the practical application of graph theory and network analysis in solving real-world social networking challenges. It showcases proficiency in algorithm design, data structure selection, and building systems that process complex relational data.