新的边周长特征增强了图神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员为图神经网络（GNN）引入了一种新的结构边特征，称为边周长（edge-girth）。该特征捕捉了通过一条边的最短环的长度和重数，旨在克服传统GNN的局限性，即其能力不比Weisfeiler-Leman颜色细化测试更强。当集成到门控消息传递架构（EGAGNN）中时，边周长在ZINC-12k回归基准测试中显著降低了测试平均绝对误差。然而，该描述符存在局限性，因为它在某些图结构中可能恒定，导致基于它的模型退回到1-WL界限，并无法区分特定的图对。 AI

影响引入了一种新颖的特征，以提高GNN在特定图相关任务上的性能。

排序理由该集群包含一篇详细介绍图神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Lilian Marey, Charlotte Laclau · 2026-09-02 04:00

Edge-Girth 作为图神经网络的结构化边缘特征

arXiv:2609.01441v1 Announce Type: new Abstract: Graph neural networks (GNN) based on message passing are provably no more powerful than the one-dimensional Weisfeiler--Leman colour-refinement test (1-WL): two graphs it cannot tell apart receive identical representations, however …

本文如何被排名

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Edge-Girth 作为图神经网络的结构化边缘特征

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