English(EN) Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs

新的图同态失真度量增强图神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员引入了一种名为图同态失真（Graph Homomorphism Distortion）的新度量，以更好地理解图学习中结构与特征之间的关系。该度量旨在量化在一个图映射到另一个图时节点特征受到的影响，解决了现有方法主要关注结构的问题。所提出的度量旨在高效近似，并提供一种区分图的精细方法，最终通过将基于同态的描述符扩展到有属性图来增强图神经网络的预测能力。 AI

影响引入了一种新颖的度量，以提高图神经网络在处理结构和特征方面的表达能力和预测能力。

排序理由 arXiv上发表的学术论文，详细介绍了图学习的新度量。[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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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Martin Carrasco, Olga Zaghen, Kavir Sumaraj, Erik Bekkers, Bastian Rieck · 2026-10-16 04:00

图同态失真：带属性图的结构（伪）度量

arXiv:2511.03068v5 Announce Type: replace Abstract: A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing approaches ignore features in favor of structure, ma…

本文如何被排名

报道来源 [1]

图同态失真：带属性图的结构（伪）度量

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