<ul><li>Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years.</li><li>This survey provides a comprehensive overview of the research at the intersection of GNNs and DEs.</li><li>The survey categorizes existing methods, discusses their underlying principles, and highlights their applications across different domains.</li><li>Open challenges and future research directions in this interdisciplinary field are also identified.</li></ul>

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks

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