Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

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dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorCamacho, David
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorYang, Xin She
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:14:18Z
dc.date.available2024-07-24T12:14:18Z
dc.date.issued2020-02
dc.descriptionPublisher Copyright: © 2019 Elsevier B.V.
dc.description.abstractDetecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti–Fortunato–Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come.en
dc.description.sponsorshipEneko Osaba and Javier Del Ser would like to thank the Basque Government, Spain for its funding support through the EMAITEK program. David Camacho also thanks the Spanish Ministry of Science , Education and Competitiveness (MINECO), Spain , the European Regional Development Fund (FEDER) and the Comunidad Autonoma de Madrid, Spain for their support under grants TIN2017-85727-C4-3-P (DeepBio) and P2018/TCS-4566 (CYNAMON). Eneko Osaba and Javier Del Ser would like to thank the Basque Government, Spain for its funding support through the EMAITEK program. David Camacho also thanks the Spanish Ministry of Science, Education and Competitiveness (MINECO), Spain, the European Regional Development Fund (FEDER) and the Comunidad Autonoma de Madrid, Spain for their support under grants TIN2017-85727-C4-3-P (DeepBio) and P2018/TCS-4566 (CYNAMON).
dc.description.statusPeer reviewed
dc.identifier.citationOsaba , E , Del Ser , J , Camacho , D , Bilbao , M N & Yang , X S 2020 , ' Community detection in networks using bio-inspired optimization : Latest developments, new results and perspectives with a selection of recent meta-heuristics ' , Applied Soft Computing Journal , vol. 87 , 106010 . https://doi.org/10.1016/j.asoc.2019.106010
dc.identifier.doi10.1016/j.asoc.2019.106010
dc.identifier.issn1568-4946
dc.identifier.urihttps://hdl.handle.net/11556/4460
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85076786258&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Soft Computing Journal
dc.relation.projectIDSpanish Ministry of Science , Education and Competitiveness
dc.relation.projectIDSpanish Ministry of Science, Education and Competitiveness
dc.relation.projectIDComunidad de Madrid, TIN2017-85727-C4-3-P-P2018/TCS-4566
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Economía y Competitividad, MINECO
dc.relation.projectIDEuropean Regional Development Fund, FEDER
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsBio-inspired computation
dc.subject.keywordsCommunity detection
dc.subject.keywordsEvolutionary computation
dc.subject.keywordsNetwork partition
dc.subject.keywordsSwarm intelligence
dc.subject.keywordsSoftware
dc.titleCommunity detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristicsen
dc.typejournal article
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