Identifying top influential spreaders based on the influence weight of layers in multiplex networks

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dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridBouyer, Asgarali/0000-0002-4808-2856;
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidZhou, Xiaohui/HNC-2193-2023
dc.authorwosidBouyer, Asgarali/IYS-5116-2023
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.contributor.authorZhou, Xiaohui
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorMaleki, Morteza
dc.contributor.authorMohammadi, Moslem
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2024-05-19T14:42:41Z
dc.date.available2024-05-19T14:42:41Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDetecting influential nodes in multiplex networks is a complex task due to the presence of multiple layers. In this study, we propose a method for identifying important layers with strong spreaders based on several key parameters. These include a layer's position within a well-connected neighborhood, the number of active edges and critical nodes, the ratio of active nodes to all possible connections, and the intersection of intra-layer communication compared to other layers. To accomplish this, we have formulated a layer weighting method which takes into account these parameters, and developed an algorithm for mapping and computing the rank of nodes based on their spreading capability within multiplex networks. The resulting layer weighting is then used to map and compress centrality vector values to a scalar value, allowing us to calculate node centrality in multiplex networks via a coupled set of equations. Moreover, our method combines the important layer parameters to compute the influence of nodes from different layers. Our experimental results, conducted on both synthetic and real-world networks, demonstrate that the proposed approach significantly outperforms existing methods in detecting high influential spreaders. These findings highlight the importance of using a suitable layer weighting measure for identifying potential spreaders in multiplex networks.en_US
dc.description.sponsorshipNational Social Science Fund Gen- eral Project [17BJY034]en_US
dc.description.sponsorshipFunding This work is supported by: 2017 National Social Science Fund Gen- eral Project (17BJY034) .en_US
dc.identifier.doi10.1016/j.chaos.2023.113769
dc.identifier.issn0960-0779
dc.identifier.issn1873-2887
dc.identifier.scopus2-s2.0-85164222865en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.chaos.2023.113769
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5271
dc.identifier.volume173en_US
dc.identifier.wosWOS:001037981200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofChaos Solitons & Fractalsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInfluential Spreadersen_US
dc.subjectEpidemic Sir Modelen_US
dc.subjectMultiplex Networksen_US
dc.subjectComplex Networksen_US
dc.titleIdentifying top influential spreaders based on the influence weight of layers in multiplex networksen_US
dc.typeArticleen_US

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