Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to handle distribution shifts, which naturally exist in dynamic graphs, mainly because the patterns exploited by DyGNNs may be variant with respect to labels under distribution shifts. In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i.e., structures and features whose predictive abilities are stable across distribution shifts. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. By utilizing the disentangled patterns, we design a spatio-temporal intervention mechanism to create multiple interventional distributions and an environment inference module to infer the latent spatio-temporal environments, and minimize the variance of predictions among these intervened distributions and environments, so that our model can make predictions based on invariant patterns with stable predictive abilities under distribution shifts. Extensive experiments demonstrate the superiority of our method over state-of-the-art baselines under distribution shifts. Our work is the first study of spatio-temporal distribution shifts in dynamic graphs, to the best of our knowledge.

翻译：动态图神经网络（DyGNNs）通过利用图结构和时间动态，展现了强大的预测能力。然而，现有的DyGNNs无法处理动态图中天然存在的分布偏移，这主要是因为DyGNNs所利用的模式在分布偏移下可能与标签存在变异。本文提出一种基于解耦干预的动态图注意力网络及其不变性提升方法（I-DIDA），通过发现并利用不变模式（即预测能力在分布偏移下保持稳定的结构和特征），来处理动态图中的时空分布偏移。具体而言，我们首先提出一种解耦的时空注意力网络，以捕获变异模式和不变模式。利用解耦模式，我们设计了一种时空干预机制来创建多种干预分布，并构建了一个环境推断模块来推断潜在的时空环境，进而最小化这些干预分布和环境下的预测方差，从而使模型能够基于分布偏移下具有稳定预测能力的不变模式进行预测。大量实验证明了我们的方法在分布偏移下优于现有最先进的基线模型。据我们所知，本文是首个针对动态图中时空分布偏移的研究。