New algorithms enhance graph clustering for complex networks

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-04 04:00

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

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

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He · 2026-06-04 04:00

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…

报道来源 [1]

Local Clustering on Complex Graphs and Complex Hypergraphs

相关话题