English(EN) The Expressive Power of Random-Feature MPNNs Across Graph Sizes

新框架分析MPNN在不同图大小上的表达能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

已开发出一个新的理论框架，用于分析随机特征消息传递神经网络（MPNN）在不同图大小上的表达能力。该框架表明，虽然连续随机特征增强了MPNN的表达能力，但这些网络并非普遍具有大小一致性。研究还证明了随机颜色MPNN与高阶Weisfeiler-Leman方法不可比较，并提出了针对特定图任务的构造方法。 AI

影响为理解基于图的机器学习任务中MPNN的能力和局限性提供了理论基础。

排序理由该集群包含一篇学术论文，详细介绍了用于分析MPNN的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了用于分析MPNN的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Robert R. Nerem, Mitchell Black, Antonis Vasileiou, Christopher Morris, Yusu Wang · 2026-10-12 04:00

随机特征MPNN在不同图规模下的表达能力

arXiv:2610.12809v1 Announce Type: new Abstract: Continuous random node features increase the expressivity of message-passing neural networks (MPNNs), but existing theoretical analyses are not fine-grained enough to capture their limitations, particularly in approximating function…

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随机特征MPNN在不同图规模下的表达能力

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