Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique. However, they are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification. To bridge the gap, prompt-based learning has gained traction on graphs, but most existing efforts focus on static graphs, neglecting the evolution of dynamic graphs. In this paper, we propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. First, we design dual prompts to address the gap in both task objectives and temporal variations across pre-training and downstream tasks. Second, we recognize that node and time features mutually characterize each other, and propose dual condition-nets to model the evolving node-time patterns in downstream tasks. Finally, we thoroughly evaluate and analyze DYGPROMPT through extensive experiments on four public datasets.

翻译：动态图捕捉实体间不断演化的交互关系，例如在社交网络、在线学习平台和众包项目中。针对动态图建模，动态图神经网络（DGNNs）已成为主流技术。然而，它们通常在链接预测任务上进行预训练，这与下游任务（如节点分类）的目标存在显著差距。为弥合这一差距，基于提示的学习在图领域受到关注，但现有研究大多集中于静态图，忽视了动态图的演化特性。本文提出DYGPROMPT，一种用于动态图建模的新型预训练与提示学习框架。首先，我们设计双重提示以解决预训练与下游任务之间在任务目标和时序变化上的差异。其次，我们认识到节点特征与时间特征相互表征，并提出双重条件网络来建模下游任务中演化的节点-时间模式。最后，我们在四个公开数据集上通过大量实验对DYGPROMPT进行了全面评估与分析。