A swarm intelligence-based hybrid approach for identifying network modules

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dc.contributor.authorAtay, Yilmaz
dc.contributor.authorAslan, Murat
dc.contributor.authorKodaz, Halife
dc.date.accessioned2020-03-26T19:52:48Z
dc.date.available2020-03-26T19:52:48Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractComplex network structures, where real-world systems are modelled, contain important information that can be uncovered. Various studies have been carried out, and many methods have been proposed recently to discover such information by using different network analysis techniques. The discovery of meaningful modules in networks is one of these significant works. In this study, a new hybrid method, which is called uniSFLA, is proposed to determine statistically significant modules within the network. Another significant aspect of this study is to use various objective functions as fitness criteria and compare the results obtained from the tests with each other. The aim is to test the success of various objective functions used to investigate network modules and those defined according to different properties in graphs. The proposed algorithm was tested on real-world networks, and the test results were compared with those of other algorithms from published literature. Considering the experimental results, the method suggested in this work produced significant success in terms of both best and average values. Moreover, the accuracy and quality tests of the conformity values obtained for each objective function were performed with four different cluster evaluation criteria. Finally, in addition to the successful results for the uniSFLA algorithm, the comparative test results of appropriate network modules, obtained using modularity and significance functions, were evaluated by means of various tables and graphs. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipScientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211/c (1649B031402383), 2214/a (1059B141500230)]; Teaching Staff Training Program Office [2013-OYP-057]en_US
dc.description.sponsorshipWe are thankful to The Scientific and Technological Research Council of Turkey for their support and finance for this project. This study is supported by TUBITAK, 2211/c (1649B031402383) and 2214/a (1059B141500230). We are also thankful to Selcuk University. This study is also supported by Teaching Staff Training Program Office (2013-OYP-057).en_US
dc.identifier.doi10.1016/j.jocs.2017.10.011en_US
dc.identifier.endpage280en_US
dc.identifier.issn1877-7503en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage265en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jocs.2017.10.011
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36294
dc.identifier.volume28en_US
dc.identifier.wosWOS:000449242900026en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofJOURNAL OF COMPUTATIONAL SCIENCEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCluster evaluationen_US
dc.subjectCommunity detectionen_US
dc.subjectComparative analysisen_US
dc.subjectModularityen_US
dc.subjectNetwork modulesen_US
dc.titleA swarm intelligence-based hybrid approach for identifying network modulesen_US
dc.typeArticleen_US

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