A fast module identification and filtering approach for influence maximization problem in social networks

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dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.authoridRouhi, Alireza/0000-0003-1494-3467
dc.authoridArasteh, Bahman/0000-0001-5202-6315;
dc.authorwosidBouyer, Asgarali/IYS-5116-2023
dc.authorwosidRouhi, Alireza/L-2209-2018
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.contributor.authorBeni, Hamid Ahmadi
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorAzimi, Sevda
dc.contributor.authorRouhi, Alireza
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2024-05-19T14:46:41Z
dc.date.available2024-05-19T14:46:41Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this paper, we explore influence maximization, one of the most widely studied problems in social network analysis. However, developing an effective algorithm for influence maximization is still a challenging task given its NP-hard nature. To tackle this issue, we propose the CSP (Combined modules for Seed Processing) algorithm, which aim to identify influential nodes. In CSP, graph modules are initially identified by a combination of criteria such as the clustering coefficient, degree, and common neighbors of nodes. Nodes with the same label are then clustered together into modules using label diffusion. Subsequently, only the most influential modules are selected using a filtering method based on their diffusion capacity. The algorithm then merges neighboring modules into distinct modules and extracts a candidate set of influential nodes using a new metric to quickly select seed sets. The number of selected nodes for the candidate set is restricted by a defined limit measure. Finally, seed nodes are chosen from the candidate set using a novel node scoring measure. We evaluated the proposed algorithm on both real-world and synthetic networks, and our experimental results indicate that the CSP algorithm outperforms other competitive algorithms in terms of solution quality and speedup on tested networks.en_US
dc.identifier.doi10.1016/j.ins.2023.119105
dc.identifier.issn0020-0255
dc.identifier.issn1872-6291
dc.identifier.scopus2-s2.0-85159053383en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.ins.2023.119105
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5574
dc.identifier.volume640en_US
dc.identifier.wosWOS:001002118000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofInformation Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSocial Networksen_US
dc.subjectInfluence Maximizationen_US
dc.subjectModule Detectionen_US
dc.subjectFilteringen_US
dc.subjectIndependent Cascade Modelen_US
dc.titleA fast module identification and filtering approach for influence maximization problem in social networksen_US
dc.typeArticleen_US

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