Efficient Greedy Algorithms for Influence Maximization in Social Networks


Jiaguo Lv, Jingfeng Guo, Huixiao Ren, Journal of Information Processing Systems Vol. 10, No. 3, pp. 471-482, Sep. 2014  

10.3745/JIPS.04.0003
Keywords: greedy algorithm, Influence Maximization, Social Network
Fulltext:

Abstract

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Jiaguo Lv, Jingfeng Guo, & Huixiao Ren (2014). Efficient Greedy Algorithms for Influence Maximization in Social Networks. Journal of Information Processing Systems, 10(3), 471-482. DOI: 10.3745/JIPS.04.0003.

[IEEE Style]
J. Lv, J. Guo and H. Ren, "Efficient Greedy Algorithms for Influence Maximization in Social Networks," Journal of Information Processing Systems, vol. 10, no. 3, pp. 471-482, 2014. DOI: 10.3745/JIPS.04.0003.

[ACM Style]
Jiaguo Lv, Jingfeng Guo, and Huixiao Ren. 2014. Efficient Greedy Algorithms for Influence Maximization in Social Networks. Journal of Information Processing Systems, 10, 3, (2014), 471-482. DOI: 10.3745/JIPS.04.0003.