research-article

Put Three and Three Together: Triangle-Driven Community Detection

Authors:

Arnau Prat-Pérez,

David Dominguez-Sal,

Josep-M. Brunat,

Josep-Lluis Larriba-PeyAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 3

Article No.: 22, Pages 1 - 42

https://doi.org/10.1145/2775108

Published: 29 January 2016 Publication History

Abstract

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly capturing such notion. The main reason is that these metrics consider the internal community edges as a set, but ignore how these actually connect the vertices of the community. We propose the Weighted Community Clustering (WCC), which is a new community metric that takes the triangle instead of the edge as the minimal structural motif indicating the presence of a strong relation in a graph. We theoretically analyse WCC in depth and formally prove, by means of a set of properties, that the maximization of WCC guarantees communities with cohesion and structure. In addition, we propose Scalable Community Detection (SCD), a community detection algorithm based on WCC, which is designed to be fast and scalable on SMP machines, showing experimentally that WCC correctly captures the concept of community in social networks using real datasets. Finally, using ground-truth data, we show that SCD provides better quality than the best disjoint community detection algorithms of the state of the art while performing faster.

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https://doi.org/10.1016/j.neucom.2024.129030
Akachar EBougteb YOuhbi BFrikh B(2025)LeaDCDInformation Sciences: an International Journal10.1016/j.ins.2024.121341686:COnline publication date: 1-Jan-2025
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Wu XWen XSun JDong YWu CWang BXiang J(2025)HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classificationBiomedical Signal Processing and Control10.1016/j.bspc.2024.106944100(106944)Online publication date: Mar-2025
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Index Terms

Put Three and Three Together: Triangle-Driven Community Detection
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 3

February 2016

358 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2888412

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

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Association for Computing Machinery

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Publication History

Published: 29 January 2016

Accepted: 01 May 2015

Revised: 01 May 2015

Received: 01 April 2014

Published in TKDD Volume 10, Issue 3

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Cited By

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https://doi.org/10.1016/j.neucom.2024.129030
Akachar EBougteb YOuhbi BFrikh B(2025)LeaDCDInformation Sciences: an International Journal10.1016/j.ins.2024.121341686:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.ins.2024.121341
Wu XWen XSun JDong YWu CWang BXiang J(2025)HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classificationBiomedical Signal Processing and Control10.1016/j.bspc.2024.106944100(106944)Online publication date: Mar-2025
https://doi.org/10.1016/j.bspc.2024.106944
Wu XWang CLin JXi WYu P(2024)Motif-Based Contrastive Learning for Community DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336787335:9(11706-11719)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2024.3367873
Liu WCai HXing HHu STan ZSong C(2024)Alleviating the resolution limit problem in spatial community detection: a local network structure-based methodInternational Journal of Geographical Information Science10.1080/13658816.2024.2421778(1-23)Online publication date: 30-Oct-2024
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Yang JZheng Y(2024)An Improved Label Propagation Algorithm Based on Motif and Critical Node for Community DetectionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5678-0_11(121-133)Online publication date: 1-Aug-2024
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Zheng ZShi XJin H(2023)Parallel Overlapping Community Detection Algorithm on GPUIEEE Transactions on Big Data10.1109/TBDATA.2022.31803609:2(677-687)Online publication date: 1-Apr-2023
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