Comparative analysis of centrality measures for identifying critical nodes in complex networks

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dc.contributor.authorUgurlu, Onur
dc.date.accessioned2023-03-22T19:47:22Z
dc.date.available2023-03-22T19:47:22Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractOne of the fundamental tasks in complex networks is detecting critical nodes whose removal significantly disrupts network connectivity. Identifying critical nodes can help analyze the topological characteristics of the network, such as vulnerability and robustness. This work considers a well-known critical node detection problem variant, Maximize the Number of Connected Components Problem, which aims to find a set of nodes whose removal maximizes the number of connected components and compares the centrality measures for detecting these nodes. While the existing literature focused only on small datasets, this work analyzes the widely used topology-based centrality measures on several synthetic and real-world networks. Our findings show that degree-like centralities are more relevant measures than path-like centralities for disconnecting networks into several connected components. However, our results also indicate that the traditional centrality measures cannot detect the most vital critical nodes. To overcome this drawback, a new centrality measure, namely Isolating Centrality, that aims to identify the nodes that significantly impact network connectedness is presented. The comprehensive computational study demonstrates that the proposed measure outperforms traditional measures in identifying critical nodes.en_US
dc.description.sponsorshipTUBITAK (Scientific and Tech-nological Research Council of Turkey) [121F092]en_US
dc.description.sponsorshipAcknowledgment This research was supported by the TUBITAK (Scientific and Tech-nological Research Council of Turkey) [Project number 121F092] .en_US
dc.identifier.doi10.1016/j.jocs.2022.101738
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.scopus2-s2.0-85132533876en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jocs.2022.101738
dc.identifier.urihttps://hdl.handle.net/20.500.14034/662
dc.identifier.volume62en_US
dc.identifier.wosWOS:000817839700013en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.journalJournal Of Computational Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComplex networksen_US
dc.subjectRobustnessen_US
dc.subjectCentrality measuresen_US
dc.subjectCritical nodesen_US
dc.subjectFrameworken_US
dc.titleComparative analysis of centrality measures for identifying critical nodes in complex networksen_US
dc.typeArticleen_US

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