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English(EN) On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry

新研究统一了图神经网络的表达能力与几何学，并探索了随机特征

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的arXiv论文探讨了图神经网络（GNNs）的理论基础。第一篇论文引入了一个使用经验Rademacher复杂度来统一GNN表达能力和几何学的框架，提供了考虑数据分布和输入空间几何学的更紧密的泛化界限。第二篇论文研究了具有随机节点特征的GNNs，为置换等变神经网络建立了普遍性结果，并推导了可微函数的逼近率。 AI

影响 这些理论进展可能带来更强大、更具泛化能力的图学习模型。

排序理由 两篇在arXiv上发表的学术论文，讨论图神经网络的理论方面。

在 arXiv cs.LG 阅读 →

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新研究统一了图神经网络的表达能力与几何学，并探索了随机特征

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两篇在arXiv上发表的学术论文，讨论图神经网络的理论方面。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan, Ehimare Okoyomon, Caterina Graziani ·

    图神经网络的Rademacher复杂度：统一表达能力与几何学

    arXiv:2510.10101v4 Announce Type: replace Abstract: Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The expressivity of Graph Neural Networks (GNNs) is typically characterized through …

  2. arXiv stat.ML TIER_1 English(EN) · Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber ·

    具有随机特征的图神经网络的普遍性和逼近率

    arXiv:2607.26699v1 Announce Type: cross Abstract: We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both theoretically and empirically. Here, we establish a …

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