Evolving relations in real-world networks are often modelled by temporal graphs. Graph rewiring techniques have been utilised on Graph Neural Networks (GNNs) to improve expressiveness and increase model performance. In this work, we propose Temporal Graph Rewiring (TGR), the first approach for graph rewiring on temporal graphs. TGR enables communication between temporally distant nodes in a continuous time dynamic graph by utilising expander graph propagation to construct a message passing highway for message passing between distant nodes. Expander graphs are suitable candidates for rewiring as they help overcome the oversquashing problem often observed in GNNs. On the public tgbl-wiki benchmark, we show that TGR improves the performance of a widely used TGN model by a significant margin. Our code repository is accessible at https://anonymous.4open.science/r/TGR-254C.

翻译：现实世界网络中不断演化的关系通常用时序图进行建模。图重布线技术已在图神经网络（GNN）中得到应用，以增强表达能力并提升模型性能。本文提出时序图重布线（TGR），这是首个针对时序图的图重布线方法。TGR通过利用扩展图传播，在连续时间动态图中为时序上相距较远的节点之间构建消息传递的高速通道，从而实现远距离节点间的通信。扩展图是重布线的理想选择，因为它们有助于缓解GNN中常见的过度挤压问题。在公开基准tgbl-wiki上，我们证明了TGR显著提升了广泛使用的TGN模型的性能。我们的代码仓库可在 https://anonymous.4open.science/r/TGR-254C 访问。