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Community structure: A comparative evaluation of community detection methods

Published online by Cambridge University Press:  03 January 2020

Vinh Loc Dao ,
Cécile Bothorel Open the ORCID record for Cécile Bothorel [Opens in a new window]  and
Philippe Lenca
Vinh Loc Dao
Affiliation:
IMT Atlantique, Lab-STICC CNRS UMR 6285, F-29238, Brest, France, (e-mails: loc.daovinh@gmail.com; philippe.lenca@imt-atlantique.fr)
Cécile Bothorel*
Affiliation:
IMT Atlantique, Lab-STICC CNRS UMR 6285, F-29238, Brest, France, (e-mails: loc.daovinh@gmail.com; philippe.lenca@imt-atlantique.fr)
Philippe Lenca
Affiliation:
IMT Atlantique, Lab-STICC CNRS UMR 6285, F-29238, Brest, France, (e-mails: loc.daovinh@gmail.com; philippe.lenca@imt-atlantique.fr)
*
*Corresponding author. Email: cecile.bothorel@imt-atlantique.fr
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Abstract

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

Keywords

community detectioncommunity structurecomparative analysisempirical analysiscomputation timecommunity sizestructural quality functionvalidation metricdecision-making assistance for practitioners
Type
Research Article
Information
Network Science , Volume 8 , Issue 1 , March 2020 , pp. 1 - 41
Copyright
© Cambridge University Press 2020

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