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English(EN) Full-Spectrum Graph Neural Network: Expressive and Scalable

全谱GNN推进了用于富有表现力和可扩展图学习的光谱滤波

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出全谱图神经网络（FSpecGNN），这是一种新颖的方法，可增强光谱图神经网络的表现力。FSpecGNN通过在节点对上操作并对特征值对使用双变量滤波器来推广经典光谱GNN，超越了一维Weisfeiler-Lehman测试的局限性。这一进展实现了节点对信号的通用近似，这对于异质图学习特别有益。所提出的架构还集成了可扩展的实现和低秩近似，从而能够在大规模图上进行高效学习。 AI

影响 引入了一种更具表现力和可扩展性的图神经网络架构，有可能提高在异质图学习任务上的性能。

排序理由 这是一篇介绍图神经网络新模型架构的研究论文。[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 →

全谱GNN推进了用于富有表现力和可扩展图学习的光谱滤波

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这是一篇介绍图神经网络新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaohan Wang, Deyu Bo, Longlong Li, Kelin Xia ·

    全谱图神经网络：富有表现力且可扩展

    arXiv:2605.05759v1 Announce Type: new Abstract: It is well established that spectral graph neural networks (GNNs) can universally approximate node signals; however, their expressive power remains bounded by the 1-dimensional Weisfeiler-Lehman test, which is mirrored in their lack…

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