Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks

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dc.contributor.authorŞimşek, Aybike
dc.contributor.authorKara, Resul
dc.date.accessioned2020-04-30T23:46:52Z
dc.date.available2020-04-30T23:46:52Z
dc.date.issued2018
dc.departmentDÜ, Gölyaka Meslek Yüksekokulu, Bilgisyar Programcılığı Bölümüen_US
dc.descriptionKara, Resul/0000-0001-8902-6837; Simsek, Aybike/0000-0002-1033-1597en_US
dc.descriptionWOS: 000446949300017en_US
dc.description.abstractPeople use online social networks to exchange information, spread ideas, learn about innovations, etc. Thus, it is important to know how information spreads through social networks. It is possible to spread information (e.g., product advertisement) to a larger number of individuals via a social network. Similarly, it is possible to minimize the spread of unwanted content (e.g., 'false news'). The key point in both cases is to identify the most influential individuals on the social network. This problem is named as Influence Maximization (IM) problem. The IM problem focuses on finding the small subset of individuals in a social environment who influence a certain group of individuals, i.e., maximize/minimize the spreading of information. Some greedy algorithms, stochastic algorithms and evolutionary optimization algorithms have been developed to find a solution to this problem. However, these methods are not at the desired level in terms of speed or solution capability. On the other hand, although many swarm intelligence algorithms that produce fast and optimal solutions can be found in the literature, these algorithms cannot be directly applied to the IM problem because no general slope is produced on the state-space surface of the IM problem's objective function. Swarm intelligence algorithms follow the general slope over the surface to converge at the global optimum. Thus, they cannot converge to the global optimum in the IM problem. In this study, a change in the structure of the IM problem is suggested in order to tailor it to swarm intelligence algorithms and to achieve a general slope on the state-space surface of its objective function. We named this process as "reshaping". More precisely, if a social network is envisioned as a graph and individuals as nodes, reshaping means sorting the nodes in descending order (from largest to smallest) according to the metrics under consideration (i.e., metrics that give an idea about the level of influence of an individual) and renumbering the nodes according to this order. Thus, the nodes those are close to each other in terms of level of influence become closer to each other in the state-space. This creates a general slope on the state-space surface of the objective function. This simple idea paves the way for applying all swarm intelligence algorithms to this kind of problem. The proposed approach was tested with real and synthetic graphs. The experiments employed the Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) as the swarm intelligence algorithms and PageRank and Kempe et al.'s Greedy Algorithm as benchmark methods. Experimental results showed that this approach worked well. (C) 2018 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2018.07.038en_US
dc.identifier.endpage236en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage224en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2018.07.038
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5332
dc.identifier.volume114en_US
dc.identifier.wosWOS:000446949300017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science 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.subjectSocial influence analysisen_US
dc.subjectInfluential individualsen_US
dc.subjectInfluence maximizationen_US
dc.subjectSocial networken_US
dc.subjectSwarm intelligenceen_US
dc.subjectOptimizationen_US
dc.subjectLarge networksen_US
dc.subjectComplex networksen_US
dc.titleUsing swarm intelligence algorithms to detect influential individuals for influence maximization in social networksen_US
dc.typeArticleen_US

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