Identifying influential nodes based on new layer metrics and layer weighting in multiplex networks

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dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.authoridArasteh, Bahman/0000-0001-5202-6315;
dc.authorwosidBouyer, Asgarali/IYS-5116-2023
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorMohammadi, Moslem
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2024-05-19T14:45:54Z
dc.date.available2024-05-19T14:45:54Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIdentifying influential nodes in multiplex complex networks have a critical importance to implement in viral marketing and other real-world information diffusion applications. However, selecting suitable influential spreaders in multiplex networks are more complex due to existing multiple layers. Each layer of multiplex networks has its particular importance. Based on this research, an important layer with strong spreaders is a layer positioned in a well-connected neighborhood with more active edges, active critical nodes, the ratio of active nodes and their connections to all possible connections, and the intersection of intralayer communication compared to other layers. In this paper, we have formulated a layer weighting method based on mentioned layer's parameters and proposed an algorithm for mapping and computing the rank of nodes based on their spreading capability in multiplex networks. Thus, the result of layer weighting is used in mapping and compressing centrality vector values to a scalar value for calculating the centrality of nodes in multiplex networks by a coupled set of equations. In addition, based on this new method, the important layer parameters are combined for the first time to utilize in computing the influence of nodes from different layers. Experimental results on both synthetic and real-world networks show that the proposed layer weighting and mapping method significantly is effective in detecting high influential spreaders against compared methods. These results validate the specific attention to suitable layer weighting measure for identifying potential spreaders in multiplex network.en_US
dc.identifier.doi10.1007/s10115-023-01983-7
dc.identifier.endpage1035en_US
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85171975066en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1011en_US
dc.identifier.urihttps://doi.org10.1007/s10115-023-01983-7
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5384
dc.identifier.volume66en_US
dc.identifier.wosWOS:001070590000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofKnowledge and Information Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectMultiplex Networksen_US
dc.subjectShell Decompositionen_US
dc.subjectInfluential Nodesen_US
dc.subjectIntralayer Densityen_US
dc.subjectLayer Weightingen_US
dc.titleIdentifying influential nodes based on new layer metrics and layer weighting in multiplex networksen_US
dc.typeArticleen_US

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