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:神经网络图(GNNs)已成为学习(静态)图形结构数据的主要范例,但许多现实世界系统具有动态性质,因为图形和节点/前沿属性随时间变化而变化,近年来,基于GNN的时空图形模型成为扩大全球NNs能力的一个大有希望的研究领域。在这项工作中,我们首次全面概述了目前暂时GNN的最新技术,引进了严格的学习设置和任务正规化,并采用了新颖的分类法,从时间方面如何代表并处理现有方法。我们通过从研究和应用角度讨论该领域最相关的公开挑战来结束调查。