Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83550
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dc.contributor.authorTang, Jingen
dc.contributor.authorTang, Xueyanen
dc.contributor.authorYuan, Junsongen
dc.date.accessioned2017-07-19T04:56:32Zen
dc.date.accessioned2019-12-06T15:25:25Z-
dc.date.available2017-07-19T04:56:32Zen
dc.date.available2019-12-06T15:25:25Z-
dc.date.issued2017en
dc.identifier.citationTang, J., Tang, X., & Yuan, J. (2017). Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach. 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).en
dc.identifier.urihttps://hdl.handle.net/10356/83550-
dc.description.abstractInfluence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.format.extent8 p.en
dc.language.isoenen
dc.rights© 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.en
dc.subjectOnline social networksen
dc.subjectInfluence maximizationen
dc.titleInfluence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approachen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en
dc.contributor.conference2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)en
dc.description.versionAccepted versionen
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