Link Prediction in Citation Networks
This project presents our solution for the CentraleSupélec Data Science Kaggle Competition, where we achieved 2nd Place for the task of link prediction in citation networks.
The goal of the project was to predict missing links between papers in a citation graph using machine learning and graph-based methods. The task required understanding the structure of citation networks and designing models that could effectively infer whether an edge should exist between two nodes.
Our approach focused on leveraging graph representations and learning meaningful relationships between papers based on the network structure. This project highlights the use of Graph Neural Networks (GNNs) and related techniques for relational learning on graph data.
You can view the full winning solution here:
