Research Network

Objective: Build a basic simulation of a researcher-focused social network, using object-oriented programming and data structures to handle user profiles, relationships, and intelligent connection recommendations.

Key Components:

• • Profile Management System: Created a custom class to represent researcher profiles with details like name, email, research interests, and past experience.

• • Data Structuring with BST: Used a Binary Search Tree to store and retrieve profiles efficiently, allowing quick lookup and alphabetical navigation.

• • Graph-Based Connections: Designed a graph to simulate follower/following relationships between researchers and model their network structure..

• • File Integration: Developed a file reader to import profile data from structured text files and populate the system automatically.

• • Alphabetical Sorting Mechanism: Implemented a sorting mechanism to help users browse profiles in a structured, organized way.

• • Follower Recommendations: Implemented a simple recommendation system using the triadic closure principle—suggesting new connections based on shared contacts.

Key Outcomes: 

• • Learned how to combine OOP, BSTs, and graphs into a cohesive tool that mirrors real-world systems like LinkedIn or ResearchGate.
  • Practiced thinking in terms of data relationships, not just data points—crucial for systems that grow over time.

  • The project showed potential for low-resource academic platforms that focus on discoverability, collaboration, and profile clarity.

Explore More: Visit the project repository on GitHub for detailed insights: Researcher’s Network on GitHub