KO: Modularity optimization in community detection

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dc.authoridKarci, Ali/0000-0002-8489-8617
dc.authoridOztemiz, Furkan/0000-0001-5425-3474
dc.authorwosidKarci, Ali/AAG-5337-2019
dc.authorwosidOztemiz, Furkan/KOD-2246-2024
dc.contributor.authorOztemiz, Furkan
dc.contributor.authorKarci, Ali
dc.date.accessioned2024-08-04T20:53:22Z
dc.date.available2024-08-04T20:53:22Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMany algorithms have been developed to detect communities in networks. The success of these developed algorithms varies according to the types of networks. A community detection algorithm cannot always guarantee the best results on all networks. The most important reason for this is the approach algorithms follow when dividing any network into communities (sub-networks). The modularity of the network determines the quality of communities in networks. It is concluded that networks with high modularity values are divided into more successful communities (clusters, sub-networks). This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The basic approach of the proposed algorithm is to transfer nodes at the community boundary to neighboring communities if they meet the specified conditions. The method called KO (Karci-Oztemiz) optimization algorithm maximizes the modularity value of any community detection algorithm in the best case, while it does not change the modularity value in the worst case. For the KO algorithm's test, in this study, Walktrap, Cluster Edge Betweenness, Label Propagation, Fast Greedy, and Leading Eigenvector community detection algorithms have been applied on three popular networks that were unweighted and undirected previously used in the literature. The community structures created by five community detection algorithms were optimized via the KO algorithm and the success of the proposed method was analyzed. When the results are examined, the modularity values of the community detection algorithms applied on the three different networks have increased at varying rates (0%,.,14.73%).en_US
dc.identifier.doi10.1007/s00521-023-08284-8
dc.identifier.endpage11087en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue15en_US
dc.identifier.scopus2-s2.0-85146613290en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11073en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08284-8
dc.identifier.urihttps://hdl.handle.net/11616/101124
dc.identifier.volume35en_US
dc.identifier.wosWOS:000914924400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCommunity modularityen_US
dc.subjectCommunity detection algorithmsen_US
dc.subjectGraph networken_US
dc.subjectModularity optimizationen_US
dc.titleKO: Modularity optimization in community detectionen_US
dc.typeArticleen_US

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