Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3094
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dc.contributor.authorKoç, İsmail-
dc.date.accessioned2022-10-08T20:51:32Z-
dc.date.available2022-10-08T20:51:32Z-
dc.date.issued2022-
dc.identifier.issn0952-1976-
dc.identifier.issn1873-6769-
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2022.105202-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3094-
dc.description.abstractCommunity detection (CD) is critical to understanding complex networks. Researchers have made serious efforts to develop efficient CD algorithms in this sense. Since community detection is an NP-hard problem, utilizing metaheuristic algorithms is preferred instead of classical approaches in solving the problem. For this reason, in this study, six different metaheuristic algorithms called Archimedes optimization algorithm (AOA), Atom search optimization (ASO), Coot Bird Natural Life Model (COOT), Harris Hawks Optimization (HHO), Slime Mould Algorithm (SMA) and Arithmetic Optimization Algorithm (AROA) are used in the solution of CD problems and all of which have been proposed for solving continuous problems in recent years. Since the CD problem has a discrete structure, discrete versions of all the algorithms are produced, and then the proposed discrete algorithms are adapted to the problem. In addition, in the phase of evaluating the objective function of the problem, a fast approach based on CommunityID is proposed to minimize the time cost when solving the problem, and this approach is utilized in all the algorithms when calculating the fitness value. In the experimental studies, firstly, the novel discrete algorithms are compared with each other in terms of solution quality and time and according to these results, COOT becomes the most effective and very fast algorithm. Then, the results obtained by COOT are compared with those of important studies in the literature. When compared in terms of solution quality, it is seen that the COOT algorithm is more effective than the other algorithms. In addition, it is quite obvious that all of the proposed algorithms using the CommunityID-based approach are faster than the other algorithms in the literature in terms of time. As a result, it can be said that COOT can be an effective alternative method for dealing with CD problems. In addition, the approach based on CommunityID can also be utilized in larger networks to obtain remarkable solutions in a much shorter time.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMetaheuristic algorithmsen_US
dc.subjectCommunity detectionen_US
dc.subjectDiscrete optimizationen_US
dc.subjectGraph structuresen_US
dc.subjectSocial networksen_US
dc.subjectModularityen_US
dc.subjectAtom Search Optimizationen_US
dc.subjectFunctional Modulesen_US
dc.subjectOrganizationen_US
dc.subjectFissionen_US
dc.subjectWeben_US
dc.titleA fast community detection algorithm based on coot bird metaheuristic optimizer in social networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.engappai.2022.105202-
dc.identifier.scopus2-s2.0-85134731638en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.identifier.volume114en_US
dc.identifier.wosWOS:000838690300002en_US
dc.institutionauthorKoç, İsmail-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57190306475-
dc.identifier.scopusqualityQ1-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.grantfulltextembargo_20300101-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.13. Department of Software Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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