Correlation-enhanced Dynamic Graph Learning for Temporal Link Prediction

Temporal networks represent the evolving complex systems by regarding the contained elements as nodes and their connections as edges, respectively, which are both time-varying. Link prediction on temporal networks is an essential problem in real-world applications, which aims to forecast the evolution of temporal networks by predicting the future links to appear. However, existing methods generally focus on modeling the individual historical temporal features of source node and target node, while neglecting the complex correlations between them, thus leading to the suboptimal performance. In this paper, we propose a Correlation-enhanced Dynamic Graph learning (CoDyG) method to simultaneously take the individual features of source/target nodes and their correlations into consideration. Specifically, we achieve this by (1) introducing a co-attention network in the source/target node representation learning and (2) designing a temporal difference encoding strategy to model the temporal correlations between source/target nodes. Comprehensive experiments conducted on two widely adopted real-world temporal network datasets demonstrate that our proposed CoDyG can achieve the state-of-the-art performance in terms of the Average Precision (AP) and Area Under the Curve (AUC) metrics on the temporal link prediction task.