Dynamic graph representation learning is growing as a trending yet challenging research task owing to the widespread demand for graph data analysis in real world applications. Despite the encouraging performance of many recent works that build upon recurrent neural networks (RNNs) and graph neural networks (GNNs), they fail to explicitly model the impact of edge temporal states on node features over time slices. Additionally, they are challenging to extract global structural features because of the inherent over-smoothing disadvantage of GNNs, which further restricts the performance. In this paper, we propose a recurrent difference graph transformer (RDGT) framework, which firstly assigns the edges in each snapshot with various types and weights to illustrate their specific temporal states explicitly, then a structure-reinforced graph transformer is employed to capture the temporal node representations by a recurrent learning paradigm. Experimental results on four real-world datasets demonstrate the superiority of RDGT for discrete dynamic graph representation learning, as it consistently outperforms competing methods in dynamic link prediction tasks.

翻译：动态图表示学习因现实世界应用中对图数据分析的广泛需求，正成为一种趋势性且具有挑战性的研究任务。尽管许多基于循环神经网络（RNN）和图神经网络（GNN）的最新工作取得了令人鼓舞的性能，但它们未能明确建模边时态状态随时间切片对节点特征的影响。此外，由于GNN固有的过平滑缺陷，它们难以提取全局结构特征，这进一步限制了性能。本文提出一种循环差分图Transformer（RDGT）框架，该框架首先为每个快照中的边分配不同类别和权重，以显式描述其特定的时态状态，随后通过循环学习范式利用结构增强型图Transformer捕获时态节点表示。在四个真实世界数据集上的实验结果表明，RDGT在离散动态图表示学习中具有优越性，其在动态链接预测任务中持续优于竞争方法。