Liu Zewen, Wang Xiaoda, Wang Bohan, Huang Zijie, Yang Carl, Jin Wei
Emory University, Atlanta, GA, USA.
Amazon, Los Angeles, CA, USA.
KDD. 2025 Aug;2025:6118-6128. doi: 10.1145/3711896.3736559. Epub 2025 Aug 3.
Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field. A comprehensive paper list is provided at https://github.com/Emory-Melody/Awesome-Graph-NDEs.
图神经网络（GNNs）和微分方程（DEs）是近年来两个快速发展的研究领域，它们在最近几年展现出了显著的协同效应。图神经网络已成为用于在图结构数据上进行学习的强大工具，而微分方程为跨时空的连续动力学建模提供了一个有原则的框架。这些领域的交叉融合催生了利用两者优势的创新方法，从而实现了在物理信息学习、时空建模和科学计算等方面的应用。本综述旨在全面概述图神经网络和微分方程交叉领域中蓬勃发展的研究。我们将对现有方法进行分类，讨论其基本原理，并突出它们在分子建模、交通预测和疫情传播等领域的应用。此外，我们还确定了开放挑战，并概述了推动这一跨学科领域发展的未来研究方向。https://github.com/Emory-Melody/Awesome-Graph-NDEs提供了一份全面的论文列表。
