Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

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dc.contributor.authorAtay, Yilmaz
dc.contributor.authorKoc, Ismail
dc.contributor.authorBabaoglu, Ismail
dc.contributor.authorKodaz, Halife
dc.date.accessioned2020-03-26T19:34:25Z
dc.date.available2020-03-26T19:34:25Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big BangBig Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks - five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211C: 1649B031402383, 2214A: 1059B141500230]; Selcuk University-Academic Staff Training Program Coordination Unit (OYP Program) [2013-OYP-057]en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, 2211C: 1649B031402383 and 2214A: 1059B141500230) and Selcuk University-Academic Staff Training Program Coordination Unit (OYP Program, 2013-OYP-057).en_US
dc.identifier.doi10.1016/j.asoc.2016.11.025en_US
dc.identifier.endpage211en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage194en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2016.11.025
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34893
dc.identifier.volume50en_US
dc.identifier.wosWOS:000395834100016en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMetaheuristic optimization algorithmsen_US
dc.subjectCommunity detectionen_US
dc.subjectBiological networksen_US
dc.subjectSocial networksen_US
dc.subjectModularityen_US
dc.titleCommunity detection from biological and social networks: A comparative analysis of metaheuristic algorithmsen_US
dc.typeArticleen_US

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