English(EN) Graph Neural Networks Are Not Continuous Across Graph Resolutions

研究发现图神经网络在分辨率上缺乏连续性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-29 13:46

研究人员已经证明，图神经网络（GNNs）在不同图分辨率下并不具有一致的连续性。这种连续性缺失可能导致相似图产生截然不同的潜在表示，尤其是在表示同一对象但尺度不同时。该问题源于信息传播机制，论文提出了一种修改GNN架构的方法，以确保连续性并提高跨分辨率的泛化能力。 AI

影响这项研究突显了GNNs的一个基本局限性，可能影响其在涉及多分辨率数据的应用中的可靠性。

排序理由该集群包含一篇详细介绍理论发现和对现有模型提出修改建议的学术论文。

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael Bronstein, Daniel Cremers · 2026-06-01 04:00

Graph Neural Networks Are Not Continuous Across Graph Resolutions

arXiv:2605.31315v1 Announce Type: new Abstract: We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result, GNNs may generate substantially different latent rep…
arXiv cs.LG TIER_1 English(EN) · Daniel Cremers · 2026-05-29 13:46

图神经网络在不同图分辨率下并非连续

We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result, GNNs may generate substantially different latent representations for graphs that are very similar. I…