Maximizing the spread of influence ranking in social networks

被引：74
|
作者：
Zhu, Tian [1 ,2 ]
Wang, Bai [2 ]
Wu, Bin [2 ]
Zhu, Chuanxi [3 ]
机构：
[1] Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China
[2] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
[3] Nanchang Univ, Sch Sci, Nanchang 330031, Jiangxi, Peoples R China
来源：
INFORMATION SCIENCES | 2014年 / 278卷
基金：
中国国家自然科学基金;
关键词：
Data mining; Social network; Influence maximization; Information propagation; Node centrality; TIME;
D O I：
10.1016/j.ins.2014.03.070
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Information flows in a network where individuals influence each other. In this paper, we study the influence maximization problem of finding a small subset of nodes in a social network that could maximize the spread of influence. We propose a novel information diffusion model CTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the Independent Cascade Model (ICM). Furthermore, we propose a new ranking metric named SpreadRank generalized by the new information propagation model CTMC-ICM. We experimentally demonstrate the new ranking method that can, in general, extract nontrivial nodes as an influential node set that maximizes the spread of information in a social network and is more efficient than a distance-based centrality. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：535 / 544
页数：10
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