English(EN) Graph Representational Learning: When Does More Expressivity Hurt Generalization?

图神经网络：表达能力与泛化能力的权衡研究

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

一篇新论文探讨了图神经网络（GNNs）的表达能力与其泛化能力之间的关系。研究人员引入了一套预度量指标来量化图之间的结构相似性，并将这些指标与表达能力强的GNNs的性能联系起来。研究结果表明，虽然表达能力更强的GNNs可能表现更好，但除非其增加的复杂性被更大的训练数据集或减小的训练与测试数据之间的差异所抵消，否则它们可能会泛化不佳。 AI

影响为理解GNN性能提供了理论基础，可能指导未来的模型开发。

排序理由该集群包含一篇详细介绍图神经网络理论见解和经验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍图神经网络理论见解和经验结果的学术论文。[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) · Sohir Maskey, Raffaele Paolino, Fabian Jogl, Gitta Kutyniok, Johannes F. Lutzeyer · 2026-09-01 04:00

图表示学习：更强的表达能力何时会损害泛化能力？

arXiv:2505.11298v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We introduce a family of premetrics that capture differ…

本文如何被排名

报道来源 [1]

图表示学习：更强的表达能力何时会损害泛化能力？

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