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）图结构数据学习的领先范式。然而，许多现实世界系统本质上是动态的，其图结构及节点/边属性随时间变化。近年来，基于GNN的时序图模型作为扩展GNN能力的新兴研究方向备受关注。本文首次全面综述时序图神经网络的最新研究进展，严格形式化学习问题与任务定义，并基于时序信息的表征与处理方式提出创新性分类体系。最后，从研究与应用双重视角探讨该领域最具挑战性的开放性问题。