Feb. 13, 2024, 5:44 a.m. | Bohang Zhang Shengjie Luo Liwei Wang Di He

cs.LG updates on arXiv.org arxiv.org

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-structured data. While numerous approaches have been proposed to improve GNNs in terms of the Weisfeiler-Lehman (WL) test, generally there is still a lack of deep understanding of what additional power they can systematically and provably gain. In this paper, we take a fundamentally different perspective to study the expressive power of GNNs beyond the WL test. Specifically, we introduce a novel class of expressivity metrics via graph …

cs.lg data designing gnns graph graph neural networks networks neural networks power stat.ml structured data terms test understanding via

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