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dc.contributor.authorDeng, Xiaolong
dc.contributor.authorDou, Yingtong
dc.contributor.authorLv, Tiejun
dc.contributor.authorQuoc, Viet Hung Nguyen
dc.date.accessioned2018-08-30T12:30:23Z
dc.date.available2018-08-30T12:30:23Z
dc.date.issued2017
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2017.2764750
dc.identifier.urihttp://hdl.handle.net/10072/370497
dc.description.abstractAbstract: The research of social influence is an important topic in online social network analysis. Influence maximization is the problem of finding k nodes that maximize the influence spread in a specific social network. Robust influence maximization is a novel topic that focuses on the uncertainty factors among the influence propagation models and algorithms. It aims to find a seed set with a definite size that has robust performance with different influence functions under various uncertainty factors. In this paper, we propose a centrality-based edge activation probability evaluation method in the independent cascade model. We consider four different types of centrality measurement methods and add a modification coefficient to evaluate the edge probability. We also propose two algorithms, called NewDiscount and GreedyCIC, by incorporating the edge probability space into previous algorithms. With extensive experiments on various real online social network data sets, we find that our PageRank-based greedy algorithm has the best influence spreads and lowest running times, compared with other algorithms, on some large data sets. The experiment for evaluating the robustness performance shows that all algorithms have optimal robustness performance when the modification coefficient is set to 0.01 under the independent cascade model. This result suggests some further research directions under this model.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom22119
dc.relation.ispartofpageto22131
dc.relation.ispartofjournalIEEE Access
dc.relation.ispartofvolume5
dc.subject.fieldofresearchMachine learning
dc.subject.fieldofresearchData structures and algorithms
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode4611
dc.subject.fieldofresearchcode461305
dc.subject.fieldofresearchcode40
dc.titleA novel centrality cascading based edge parameter evaluation method for robust influence maximization
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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