English(EN) Message-passing graph persistence

新的消息传递图持久性方法增强了GNN的表达能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出了一种新颖的消息传递图持久性（MGP）方法，以增强消息传递图神经网络（GNN）的表达能力。MGP将持久同调的拓扑描述符集成到图过滤过程中，并在每一步更新节点嵌入。该方法被证明在图同构下是不变的，并且比现有的持久同调描述符更具表现力，在各种图分类和回归任务上表现优异。 AI

影响增强了图神经网络的能力，有可能提高在复杂基于图的人工智能任务上的性能。

排序理由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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arXiv cs.AI TIER_1 English(EN) · Jorge Luiz Franco, Arthur Queiroz Moura, Moacir Ponti, Amauri Souza · 2026-11-05 05:00

消息传递图持久化

arXiv:2611.06801v1 Announce Type: cross Abstract: Message-passing graph neural networks (GNNs) are limited in expressive power by the one-dimensional Weisfeiler-Leman test. Persistent homology (PH) provides topological descriptors that can complement GNN representations, but exis…