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Efficient algorithms for influence maximization in social networks

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Abstract

In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.

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Acknowledgments

Suh-Yin Lee was supported by the National Science Council, Project No. NSC99-2221-E-009-128-MY2. Wen-Chih Peng was supported in part by the National Science Council, Project No. 100-2218-E-009-016-MY3 and 100-2218-E-009-013-MY3, by Taiwan MoE ATU Program, by ITRI JRC, Project No. B352BW3300, by D-Link and by Microsoft.

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  1. Department of Computer Science, National Chiao Tung University, Hsinchu, 300, Taiwan

    Yi-Cheng Chen, Wen-Chih Peng & Suh-Yin Lee

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  1. Yi-Cheng Chen
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  2. Wen-Chih Peng
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Correspondence to Yi-Cheng Chen.

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Chen, YC., Peng, WC. & Lee, SY. Efficient algorithms for influence maximization in social networks. Knowl Inf Syst 33, 577–601 (2012). https://doi.org/10.1007/s10115-012-0540-7

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