Fine-Grained Interactive Transformers for Continuous Dynamic Link Prediction.

Abstract

DLP plays a critical role in understanding and forecasting evolving relationships in real-world systems across various domains. However, accurately predicting future links remains challenging, as existing methods often overlook the independent modeling of dynamic interactions within individual nodes and the fine-grained characterization of latent interactions across node sequences. To address these challenges, we propose FineFormer (Fine-grained Interactive Transformer), a novel framework that alternates between self-attention and cross-attention mechanisms, enhanced with layer-wise contrastive learning. This design enables FineFormer to uncover fine-grained temporal dependencies both within single node sequences and across different node sequences. Specifically, self-attention captures temporal-spatial dynamics within the interaction sequences of individual nodes, while cross-attention focuses on the complex interactions across the sequences of pairs of nodes. Additionally, by strategically applying layer-wise contrastive learning, FineFormer refines node representations and enhances the model's ability to distinguish between connected and unconnected node pairs during feature refinement. FineFormer is evaluated on five challenging and diverse real-world dynamic link prediction (DLP) datasets. Experimental results demonstrate that FineFormer consistently outperforms state-of-the-art baselines, particularly in capturing complex, fine-grained interactions in continuous-time dynamic networks.