Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.

翻译：图神经网络（GNN）已成为（静态）图结构数据学习领域的领先范式。然而，许多真实世界系统本质上具有动态特性，其图结构及节点/边属性会随时间变化。近年来，面向时序图的GNN模型作为拓展GNN能力的新兴研究方向已崭露头角。本文首次全面梳理时序图神经网络的最新研究进展，提出严格的形式化学习框架定义与任务分类体系，并构建创新性的分类法，从时间维度表征与处理方式两个层面系统归类现有方法。最后，我们从研究与应用双重视角出发，探讨该领域最具相关性的开放挑战。