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（GNNs）已成为处理（静态）图结构数据的主流范式。然而，许多真实世界的系统天然具有动态特性，其图结构及节点/边属性会随时间演变。近年来，基于GNN的时序图模型作为拓展GNN能力的研究方向崭露头角。本文首次系统梳理了时序图神经网络领域的研究现状，建立了学习场景与任务的严谨形式化定义，并提出了一个新型分类体系，根据时间维度的表示与处理方式对现有方法进行归类。最后，本文从研究与应用双重视角探讨了该领域最具相关性的开放挑战。