Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83550
Title: Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Authors: Tang, Jing
Tang, Xueyan
Yuan, Junsong
Keywords: Online social networks
Influence maximization
Issue Date: 2017
Source: Tang, 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).
Abstract: Influence 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.
URI: https://hdl.handle.net/10356/83550
http://hdl.handle.net/10220/42935
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers
SCSE Conference Papers

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