WePred: Edge Weight-Guided Contrastive Learning for Bipartite Link Prediction

Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns in sparse networks. We propose WePred, a weight-guided contrastive learning method for link prediction which addresses both challenges through three key components: (1) a weight-guided edge attention mechanism that incorporates edge weights into neighbor aggregation using dynamic attention scores, enabling the fine-grained capture of interaction strengths; (2) a dual-level contrastive learning approach that combines edge-level and node-level contrasts to capture both local weighted patterns and global structural dependencies, which is particularly effective in sparse regions; and (3) a unified learning framework that integrates classification and contrastive objectives. We evaluate WePred on five real-world datasets. The experimental results show that WePred consistently outperformed state-of-the-art methods, achieving improvements of 7.27% in the AUC on ML-1M and 3.18% in the AUC on the sparsest dataset of Amazon-Book (density: 0.001%). Ablation studies confirm the effectiveness of each component, while parameter analyses provide deployment guidance.