English(EN) Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

FilterMoE通过联合节点-通道自适应滤波增强PPGNN

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种名为FilterMoE的新型预传播图神经网络（PPGNN）方法。该方法解决了为什么更复杂的聚合器在PPGNN中并不总是优于更简单的聚合器的难题。FilterMoE引入了一种混合专家设计，该设计将Chebyshev滤波器专家联合路由到节点和通道上，在11个基准测试中的9个上表现优于现有的PPGNN。 AI

影响引入了一种新颖的图神经网络路由机制，有望提高各种基于图的任务的性能。

排序理由该集群包含一篇详细介绍图神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍图神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zichao Yue, Zhiru Zhang · 2026-06-02 04:00

过滤节点，而非信息：用于预传播 GNN 的节点-通道混合模型

arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop features, which makes them highly scalable. A puzzle in this regime is that more…

本文如何被排名

报道来源 [1]

过滤节点，而非信息：用于预传播 GNN 的节点-通道混合模型

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