TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism 事件

TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism arXiv:2605.24971v1 Announce Type: cross Abstract: The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundamental challenges in capturing long-term dependencies and identifying periodic patterns. To address these limitations, we propose TGFormer, a novel Transformer architecture specifically designed