Temporal Link Prediction with Community-Level Information

Temporal graphs aim to characterize the dynamic interactions among entities in real-world scenarios, while temporal link prediction is an essential method for modeling such relationships. Existing representation learning-based methods for temporal graph link prediction typically involve designing temporal graph neural networks to model interactions between nodes. The message-passing mechanisms of graph neural networks are incorporated with temporal information, enabling the models to generate time-specified embeddings for link prediction tasks. However, existing methods only consider interactions between nodes, neglecting the prevalent community structure in temporal graphs. To address this limitation, this paper proposes a temporal link prediction algorithm, TLPC (temporal link prediction enhanced by community-level information). Unlike traditional community detection methods, this paper focuses on representing the neighbor communities of a node. Leveraging contrastive learning techniques on top of node features, positive and negative samples are sampled based on neighborhood structural features for contrastive learning constraints, encoding the neighbor communities of nodes. The learnt encoding of node neighborhood communities effectively enhances the representation ability of node embeddings, thereby improving link prediction effectiveness. Experimental results on four real-life datasets for temporal link prediction tasks demonstrate that TLPC achieves an average accuracy increase of 6.47% and an average F1 score increase of 6.77% compared with existing methods, while simultaneously reducing training time by an average of 62.27%.