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English(EN) Local Clustering on Complex Graphs and Complex Hypergraphs

新算法增强复杂网络图聚类

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了新的算法 GeneralACL 和 HyperACL，以改进复杂图和超图上的局部聚类。这些算法扩展了经典的 Andersen-Chung-Lang (ACL) 方法，以处理加权、有向和自环图，以及具有边依赖顶点权重的超图。新方法在温和条件下被证明在电导率方面能找到二次最优聚类，并提供了实验验证。 AI

排序理由 该聚类包含一篇详细介绍图和超图聚类新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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新算法增强复杂网络图聚类

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该聚类包含一篇详细介绍图和超图聚类新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He ·

    复杂图和复杂超图上的局部聚类

    arXiv:2412.03008v2 Announce Type: replace-cross Abstract: Local/seeded clustering aims to find a compact cluster near the given starting instances. While most existing studies on graph clustering assume a discrete graph setting (i.e., unweighted, undirected graphs without self-lo…

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