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dc.contributor.authorLozano Osorio, Isaac
dc.contributor.authorCordón García, Óscar 
dc.date.accessioned2021-10-25T08:42:38Z
dc.date.available2021-10-25T08:42:38Z
dc.date.issued2021-09-30
dc.identifier.citationLozano-Osorio, I... [et al.]. A quick GRASP-based method for influence maximization in social networks. J Ambient Intell Human Comput (2021). [https://doi.org/10.1007/s12652-021-03510-4]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/71071
dc.descriptionThis research was funded by "Ministerio de Ciencia, Innovacion y Universidades" under grant ref. PGC2018-095322-B-C22, "Comunidad de Madrid" and "Fondos Estructurales" of European Union with grant refs. S2018/TCS-4566, Y2018/EMT-5062. It is also supported by the Spanish Agencia Estatal de Investigacion, the Andalusian Government, the University of Granada, and European Regional Development Funds (ERDF) under grants EXASOCO (PGC2018-101216-B-I00) and AIMAR (A-TIC- 284-UGR18).es_ES
dc.description.abstractThe evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an NP-hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.es_ES
dc.description.sponsorshipMinisterio de Ciencia, Innovacion y Universidades PGC2018-095322-B-C22es_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.description.sponsorship"Fondos Estructurales" of European Union S2018/TCS-4566 Y2018/EMT-5062es_ES
dc.description.sponsorshipSpanish Agencia Estatal de Investigaciones_ES
dc.description.sponsorshipAndalusian Governmentes_ES
dc.description.sponsorshipUniversity of Granadaes_ES
dc.description.sponsorshipEuropean Commission PGC2018-101216-B-I00 A-TIC- 284-UGR18es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectInformation systemses_ES
dc.subjectSocial networks es_ES
dc.subjectInfluence maximizationes_ES
dc.subjectNetwork sciencees_ES
dc.subjectViral marketinges_ES
dc.subjectGRASPes_ES
dc.titleA quick GRASP‑based method for influence maximization in social networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s12652-021-03510-4
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España