Link Prediction using GraphSAGE

In most real-world networks, not all connections or relationships are known. Link prediction helps fill in the gaps, thus providing a more comprehensive understanding of the network. Link prediction is a crucial task in network analysis and plays a pivotal role in domains such as social networks and recommendation systems. In this article, we propose an enhanced link prediction model that leverages snscrape, a data scraping tool for near-real-time acquisition of raw real-world data, and employs GraphSAGE, a Graph Neural Network (GNN) framework which possesses the ability to learn node embeddings in large-scale graphs that capture the network's structural features and forecast new links. For link classification, we adopt the 'IP' method, enhancing the accuracy and reliability of our link predictions. Furthermore, we optimize our model's performance by utilizing the widely recognized 'ADAM' optimizer. The proposed model contributes significantly to the field of network analysis across diverse domains, addressing link prediction challenges through a data-driven approach. In real-world scenarios, our approach outperforms existing methods by effectively capturing structural features of large-scale networks and conducting comprehensive evaluations with diverse parameters, leading to enhanced accuracy and reliability of link predictions.