Efficient Greedy Algorithms for Influence Maximization in Social Networks

Jiaguo Lv, Jingfeng Guo and Huixiao Ren
Volume: 10, No: 3, Page: 471 ~ 482, Year: 2014
10.3745/JIPS.04.0003
Keywords: Greedy Algorithm, Influence Maximization, Social Network
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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.

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Cite this article
IEEE Style
Jiaguo Lv, Jingfeng Guo, and Huixiao 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, "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.