A systematic analysis of community detection in complex networks

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dc.contributor.authorGul, Haji
dc.contributor.authorAl-Obeidat, Feras
dc.contributor.authorAmin, Adnan
dc.contributor.authorTahir, Muhammad
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2022-08-01T17:37:57Z
dc.date.available2022-08-01T17:37:57Z
dc.date.issued2022-04-27
dc.description.abstractNumerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340..pt_PT
dc.identifier.citationGul, H., Al-Obeidat, F., Amin, A., Tahir, M., & Moreira, F. (2022). A systematic analysis of community detection in complex networks. Procedia Computer Science, 201, 343-350. https://doi.org/10.1016/j.procs.2022.03.046. Repositório Institucional UPT. http://hdl.handle.net/11328/4393pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.procs.2022.03.046pt_PT
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/11328/4393
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCommunity Detectionpt_PT
dc.subjectGraph Clusteringpt_PT
dc.subjectGraph Analysispt_PT
dc.subjectComplex Networkspt_PT
dc.subjectPredictionpt_PT
dc.subjectRecommendationpt_PT
dc.titleA systematic analysis of community detection in complex networkspt_PT
dc.typejournal articlept_PT
degois.publication.firstPage343pt_PT
degois.publication.lastPage350pt_PT
degois.publication.titleProcedia Computer Sciencept_PT
degois.publication.volume201pt_PT
dspace.entity.typePublicationen
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e

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