Efficient Greedy Algorithms for Influence Maximization in Social Networks

被引：1

作者：

Lv, Jiaguo ^{[2
]}

Guo, Jingfeng ^{[1
]}

Ren, Huixiao ^{[1
]}

机构：

[1] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao, Peoples R China

[2] Zaozhuang Univ, Sch Informat Sci & Engn, Zaozhuang, Peoples R China

来源：

JOURNAL OF INFORMATION PROCESSING SYSTEMS | 2014年 / 10卷 / 03期

关键词：

Greedy Algorithm; Influence Maximization; Social Network;

D O I：

10.3745/JIPS.04.0003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

(I)nfluence 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.

引用

页码：471 / 482

页数：12

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