License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2023.34
URN: urn:nbn:de:0030-drops-186871
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18687/
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Chhabra, Adil ; Fonseca Faraj, Marcelo ; Schulz, Christian

Faster Local Motif Clustering via Maximum Flows

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Abstract

Local clustering aims to identify a cluster within a given graph that includes a designated seed node or a significant portion of a group of seed nodes. This cluster should be well-characterized, i.e., it has a high number of internal edges and a low number of external edges. In this work, we propose SOCIAL, a novel algorithm for local motif clustering which optimizes for motif conductance based on a local hypergraph model representation of the problem and an adapted version of the max-flow quotient-cut improvement algorithm (MQI). In our experiments with the triangle motif, SOCIAL produces local clusters with an average motif conductance 1.7% lower than the state-of-the-art, while being up to multiple orders of magnitude faster.

BibTeX - Entry

@InProceedings{chhabra_et_al:LIPIcs.ESA.2023.34,
  author =	{Chhabra, Adil and Fonseca Faraj, Marcelo and Schulz, Christian},
  title =	{{Faster Local Motif Clustering via Maximum Flows}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{34:1--34:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18687},
  URN =		{urn:nbn:de:0030-drops-186871},
  doi =		{10.4230/LIPIcs.ESA.2023.34},
  annote =	{Keywords: local motif clustering, motif conductance, maximum flows, max-flow quotient-cut improvement}
}

Keywords: local motif clustering, motif conductance, maximum flows, max-flow quotient-cut improvement
Collection: 31st Annual European Symposium on Algorithms (ESA 2023)
Issue Date: 2023
Date of publication: 30.08.2023
Supplementary Material: Software (Code): https://github.com/LocalClustering/HeidelbergMotifClustering

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