Enhancing Link Prediction Through Graph Attention Network and Linear Discriminant Analysis

Link prediction (LP) is a prominent research topic in network science and complex network analysis, focused on predicting future connections between unconnected node pairs by examining network topology and related characteristics. This paper introduces an advanced LP model combining the graph attention network (GAT) with Jaccard similarity and node centrality measures, such as local interaction density (LID) and hubs and authority centrality (HAC). Initially, a weight matrix incorporating structural and topological information is created using a feature matrix derived from similarity measures and node centrality. Then, node embeddings are generated using GAT, allowing the model to learn detailed representations that consider local and global contexts. GAT's multi‐head attention mechanism enables the model to focus on various aspects of the node neighbourhood, capturing diverse structural information. A well‐defined dataset is created from these embeddings, representing nodes at the endpoints of edges labelled as positive or negative. Linear discriminant analysis (LDA) is applied to this well‐balanced and labelled dataset to perform classification tasks, leading to accurate link prediction. The model's performance is evaluated across eight different datasets, and the obtained results reveal the proposed model's superiority over several baseline and recently proposed LP models.