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New algorithms enhance graph clustering for complex networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms, GeneralACL and HyperACL, to improve local clustering on complex graphs and hypergraphs. These algorithms extend the classic Andersen-Chung-Lang (ACL) method to handle weighted, directed, and self-looped graphs, as well as hypergraphs with edge-dependent vertex weights. The new methods are proven to find quadratically optimal clusters in terms of conductance under mild conditions, with experimental validation provided. AI

RANK_REASON The cluster contains a research paper detailing new algorithms for graph and hypergraph clustering. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New algorithms enhance graph clustering for complex networks

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The cluster contains a research paper detailing new algorithms for graph and hypergraph clustering. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

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

    Local Clustering on Complex Graphs and Complex Hypergraphs

    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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