dc.contributor.authorTang, Jing
dc.contributor.authorTang, Xueyan
dc.contributor.authorYuan, Junsong
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).
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_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent8 p.en_US
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_US
dc.subjectOnline social networksen_US
dc.subjectInfluence maximizationen_US
dc.titleInfluence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approachen_US
dc.typeConference Paper
dc.contributor.conference2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)en_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.description.versionAccepted versionen_US

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