English(EN) Alleviating Limited Effectiveness and Multi-Class Collapse in Deep Graph-level Clustering

新的S3GLC框架通过整合全局拓扑来增强图聚类

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

研究人员推出了一种名为S$^3$GLC的新框架，旨在改进深度图级聚类。该方法解决了现有方法中的局限性，例如图同构网络无法捕捉全局拓扑以及不平衡数据集中的聚类坍塌问题。S$^3$GLC利用源自Hoffman-Wielandt不等式的新型谱签名整合全局拓扑特征，并将其与局部邻域特征融合。为了对抗聚类坍塌，它采用了一种具有Sinkhorn-Knopp归一化的平衡聚类目标。实验表明，S$^3$GLC在REDDIT-12K和OGBG-PPA等数据集上的表现优于15种最先进的方法。 AI

影响通过解决现有聚类技术的局限性来改进图分析，可能为图结构数据带来更好的洞察。

排序理由该集群包含一篇详细介绍图级聚类新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiaoyang Lin, Renchi Yang, Yuqi Chen, Xin Huang · 2026-11-05 05:00

缓解深度图级聚类中的有效性受限和多类别坍塌问题

arXiv:2611.06052v1 Announce Type: new Abstract: Given a collection of $N$ graphs, graph-level clustering (GLC) seeks to partition the $N$ graphs into semantically meaningful groups, which serves as a fundamental task in analyzing graph-structured data. While Graph Isomorphism Net…

本文如何被排名

报道来源 [1]

缓解深度图级聚类中的有效性受限和多类别坍塌问题

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