让图神经网络学会理解历史事件对节点的影响。
Event-Aware Prompt Learning for Dynamic Graphs
- 为每个节点提取历史事件，通过适配机制对齐任务需求
- 设计事件聚合机制，有效融合历史知识到节点表示中
- 可插件式接入现有方法，适合动态图建模场景
现实世界的图通常通过一系列事件演变，反映不同领域中对象间的动态交互。针对动态图学习，动态图神经网络（DGNNs）已成为主流方案。近期，提示学习方法被引入动态图建模，但现有方法多关注节点与时间的关系，忽视了历史事件的影响。本文提出EVP框架，一种面向事件的动态图提示学习方法，可作为插件集成到现有方法中，增强其利用历史事件知识的能力。首先，为每个节点提取一系列历史事件，并引入事件适配机制，将事件细粒度特征与下游任务对齐；其次，提出事件聚合机制，有效将历史知识融入节点表示。最后，在四个公开数据集上进行大量实验，验证并分析EVP的有效性。
原文摘要 · Abstract (English)
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural networks (DGNNs) have emerged as popular solutions. Recently, prompt learning methods have been explored on dynamic graphs. However, existing methods generally focus on capturing the relationship between nodes and time, while overlooking the impact of historical events. In this paper, we propose EVP, an event-aware dynamic graph prompt learning framework that can serve as a plug-in to existing methods, enhancing their ability to leverage historical events knowledge. First, we extract a series of historical events for each node and introduce an event adaptation mechanism to align the fine-grained characteristics of these events with downstream tasks. Second, we propose an event aggregation mechanism to effectively integrate historical knowledge into node representations. Finally, we conduct extensive experiments on four public datasets to evaluate and analyze EVP.
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