Student Network Analysis

This repository contains code and analysis related to a student network dataset obtained as part of a Discrete Mathematics class. The dataset captures interactions and connections between students, and the repository provides implementations of various algorithms and techniques to gain insights from this network data.

Overview

The main objectives of this project are:

1. Random Walk: Implement a random walk algorithm to identify the most influential student, termed the "Super Winner," in the network.
2. Missing Link Prediction: Apply matrix factorization techniques to predict missing connections or relationships between students in the network.
3. At-Risk Student Identification: Develop a methodology to identify students who may be at risk of academic difficulties or social isolation based on their network connections and centrality measures.

Repository Structure

• Data Preprocessing/: Scripts and code related to data preprocessing.
• Random Walk/: Implementation of the random walk algorithm for finding the Super Winner.
• Finding Missing Links/: Code for predicting missing links using matrix factorization techniques.
• Identification of At-Risk Students/: Methodology and code for identifying at-risk students based on network analysis.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

Acknowledgments

This project was developed as part of the Discrete Mathematics course at IIT Ropar. Special thanks to Dr.S.R.S Iyengar for providing the dataset and guidance.