标题： 时空数据的图神经网络：方法与应用 显示英文标题

标题： Graph Neural Network for spatiotemporal data: methods and applications

摘要： 在大数据时代，包含丰富空间和时间信息的数据大量出现，为天气预报、自然灾害管理、智能交通系统和精准农业等应用提供了对动态系统和过程的有价值见解。图神经网络（GNNs）已成为建模和理解彼此之间存在依赖关系的数据的强大工具，例如空间和时间依赖关系。已有大量研究工作专注于利用GNNs解决时空数据中的复杂空间和时间依赖关系。然而，时空数据的强跨学科性质已经催生了许多专门针对不同应用领域的GNNs变体。尽管这些技术通常适用于各种领域，但由于缺乏关于GNNs在时空数据方面的全面文献综述，交叉参考这些方法仍然至关重要但具有挑战性。本文旨在提供GNNs在时空领域中的技术和应用的系统且全面的概述。首先，总结了从时空数据构建图的方法，以帮助领域专家了解如何从各种类型的时空数据生成图。然后，对现有的时空GNNs进行了系统分类和总结，使领域专家能够识别合适的技术，并支持模型开发者推进他们的研究。此外，还提供了时空领域中重要应用的全面概述，向模型开发者和领域专家介绍更广泛的应用范围，帮助他们探索潜在的研究主题并提高其工作的影响力。最后，讨论了开放性的挑战和未来方向。 显示英文摘要

摘要： In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed.