English(EN) Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks

新的GNN框架通过异步更新解决过压问题 · arXiv研究

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-19 04:00

研究人员开发了一种新颖的异步消息传递框架，旨在缓解图神经网络（GNN）中的过压问题。这种新方法允许层内进行顺序节点更新，与传统的同步方法形成对比，有助于信息在长距离上传播得更有效。理论分析表明，与同步模型相比，网络深度增加时敏感度边界的衰减速度更慢。实证结果显示在多个图分类基准测试中性能显著提高，包括在REDDIT-BINARY上提高了5%，在Peptides-struct上提高了4%。 AI

影响提高了GNN中的长距离交互能力，可能增强在复杂图任务上的性能。

排序理由关于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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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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arXiv cs.LG TIER_1 English(EN) · Kushal Bose, Swagatam Das · 2026-08-19 04:00

用于解决图神经网络中过压问题的异步消息传递

arXiv:2509.06777v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range interactions. Existing remedies are limited: graph rew…

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用于解决图神经网络中过压问题的异步消息传递

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