标题： TIDFormer：利用时序和交互动态造就出色的动态图Transformer 显示英文标题

标题： TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

摘要： 由于自注意力机制（SAMs）在捕捉序列建模中的依赖关系方面的能力，一些现有的动态图神经网络（DGNNs）利用带有各种编码设计的Transformer架构来捕获动态图的序列演化。 然而，这些基于Transformer的DGNNs的有效性和效率差异显著，这凸显了在动态图上正确定义SAM以及全面编码时序和交互动力学的重要性，而无需额外的复杂模块。 在这项工作中，我们提出了TIDFormer，这是一种动态图Transformer，能够高效地充分挖掘时序和交互动力学。 我们澄清并验证了所提出的SAM的可解释性，解决了先前工作中动态图上不可解释定义的开放问题。 为了分别建模时序和交互动力学，我们利用基于日历的时间划分信息，并仅使用采样的一阶邻居提取二分图和非二分图的有信息量的交互嵌入。 此外，我们通过简单分解来联合建模时序和交互特征，捕捉历史交互模式中的潜在变化。 我们在几个动态图数据集上进行了广泛的实验，以验证TIDFormer的有效性和效率。 实验结果表明，TIDFormer在大多数数据集和实验设置下优于最先进的模型。 此外，与之前的基于Transformer的方法相比，TIDFormer表现出显著的效率优势。 显示英文摘要

摘要： Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture sequential evolutions of dynamic graphs. However, the effectiveness and efficiency of these Transformer-based DGNNs vary significantly, highlighting the importance of properly defining the SAM on dynamic graphs and comprehensively encoding temporal and interactive dynamics without extra complex modules. In this work, we propose TIDFormer, a dynamic graph TransFormer that fully exploits Temporal and Interactive Dynamics in an efficient manner. We clarify and verify the interpretability of our proposed SAM, addressing the open problem of its uninterpretable definitions on dynamic graphs in previous works. To model the temporal and interactive dynamics, respectively, we utilize the calendar-based time partitioning information and extract informative interaction embeddings for both bipartite and non-bipartite graphs using merely the sampled first-order neighbors. In addition, we jointly model temporal and interactive features by capturing potential changes in historical interaction patterns through a simple decomposition. We conduct extensive experiments on several dynamic graph datasets to verify the effectiveness and efficiency of TIDFormer. The experimental results demonstrate that TIDFormer excels, outperforming state-of-the-art models across most datasets and experimental settings. Furthermore, TIDFormer exhibits significant efficiency advantages compared to previous Transformer-based methods.