Analysis of influence maximization in large-Scale social networks

被引：1

作者：

Hu, Jie ^{[1
]}

Meng, Kun ^{[1
]}

Chen, Xiaomin ^{[2
]}

Lin, Chuang ^{[1
]}

Huang, Jiwei ^{[1
]}

机构：

[1] Department of Computer Science and Technology, Tsinghua University, Beijing

[2] School of Information Engineering, University of Science and Technology Beijing, Beijing

来源：

Performance Evaluation Review | 2014年 / 41卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Influence Maximization; Series-Parallel Graph; Social Network;

D O I：

10.1145/2627534.2627559

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Influence maximization is an important problem in online social networks. With the scale of social networks increasing, the requirements of solutions for influence maximization are becoming more and more strict. In this paper, we discuss two basic methods to compute the influence in general social networks, and then reveal that the computation of influence in series-parallel graph is in linear time complexity. Finally, we propose an novel method to solve influence maximization and show that it has a good performance.

引用

页码：78 / 81

页数：3

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