English(EN) Universality and Approximation Rates of Graph Neural Networks with Random Features

具有随机特征的图神经网络实现普遍性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 09:46

研究人员为结合了随机节点特征的消息传递图神经网络（GNN）建立了一个新的普遍性结果。这项工作特别关注置换等变神经网络（PENN），这是一类广泛的GNN，包括许多流行的架构。研究结果表明，当用部分随机节点特征增强时，PENN可以有效地逼近固定大小有向图上的任何可测量的置换不变或置换等变函数，前提是它们具有多维节点和边特征。此外，该研究为连续可微函数的逼近率提供了界限，将PENN的前馈组件的复杂性与逼近的准确性联系起来。 AI

影响确立了图神经网络的理论极限和能力，可能影响基于图数据的未来模型架构。

排序理由详细介绍图神经网络理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-29 09:46

具有随机特征的图神经网络的普遍性和逼近率

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 novel universality result focusing on permutation-…

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具有随机特征的图神经网络的普遍性和逼近率

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