FIP: A fast overlapping community-based influence maximization algorithm using probability coefficient of global diffusion in social networks

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dc.authoridBahman Arasteh / 0000-0001-5202-6315
dc.authorscopusidBahman Arasteh / 39861139000en_US
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorAhmadi Beni, Hamid
dc.contributor.authorArasteh, Bahman
dc.contributor.authorAghaee, Zahra
dc.contributor.authorGhanbarzadeh, Reza
dc.date.accessioned2022-10-31T09:44:21Z
dc.date.available2022-10-31T09:44:21Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractInfluence maximization is the process of identifying a small set of influential nodes from a complex network to maximize the number of activation nodes. Due to the critical issues such as accuracy, stability, and time complexity in selecting the seed set, many studies and algorithms has been proposed in recent decade. However, most of the influence maximization algorithms run into major challenges such as the lack of optimal seed nodes selection, unsuitable influence spread, and high time complexity. In this paper intends to solve the mentioned challenges, by decreasing the search space to reduce the time complexity. Furthermore, It selects the seed nodes with more optimal influence spread concerning the characteristics of a community structure, diffusion capability of overlapped and hub nodes within and between communities, and the probability coefficient of global diffusion. The proposed algorithm, called the FIP algorithm, primarily detects the overlapping communities, weighs the communities, and analyzes the emotional relationships of the community's nodes. Moreover, the search space for choosing the seed nodes is limited by removing insignificant communities. Then, the candidate nodes are generated using the effect of the probability of global diffusion. Finally, the role of important nodes and the diffusion impact of overlapping nodes in the communities are measured to select the final seed nodes. Experimental results in real-world and synthetic networks indicate that the proposed FIP algorithm has significantly outperformed other algorithms in terms of efficiency and runtime.en_US
dc.identifier.citationBouyer, A., Ahmadi Beni, H., Arasteh, B., Aghaee, Z., & Ghanbarzadeh, R. (2023). FIP: A fast overlapping community-based influence maximization algorithm using probability coefficient of global diffusion in social networks. Expert Systems with Applications, 213 doi:10.1016/j.eswa.2022.118869en_US
dc.identifier.doi10.1016/j.eswa.2022.118869en_US
dc.identifier.scopus2-s2.0-85139362517en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.118869
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3212
dc.identifier.volume213en_US
dc.identifier.wosWOS:000874659300010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArasteh, Bahman
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCommunity Detectionen_US
dc.subjectInfluence Maximizationen_US
dc.subjectOverlapping Nodesen_US
dc.subjectProbability Coefficient of Global Diffusionen_US
dc.subjectSocial Networksen_US
dc.titleFIP: A fast overlapping community-based influence maximization algorithm using probability coefficient of global diffusion in social networksen_US
dc.typeArticleen_US

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