Minimizing the expected complete influence time of a social network

被引：27

作者：

Ni, Yaodong ^{[1
,2
]}

Xie, Lei ^{[3
]}

Liu, Zhi-Qiang ^{[2
]}

机构：

[1] Univ Int Business & Econ, Sch Informat Technol & Management, Beijing 100029, Peoples R China

[2] City Univ Hong Kong, Sch Creat Media, Hong Kong, Hong Kong, Peoples R China

[3] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China

来源：

INFORMATION SCIENCES | 2010年 / 180卷 / 13期

关键词：

Social networks; Complete influence; Spanning forest; Stochastic simulation; Greedy algorithm; SYSTEMS;

D O I：

10.1016/j.ins.2010.03.012

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Complete influence time specifies how long it takes to influence all individuals in a social network, which plays an important role in many real-life applications. In this paper, we study the problem of minimizing the expected complete influence time of social networks. We propose the incremental chance model to characterize the diffusion of influence, which is progressive and able to achieve complete influence. Theoretical properties of the expected complete influence time under the incremental chance model are presented. In order to trade off between optimality and complexity, we design a framework of greedy algorithms. Finally, we carry out experiments to show the effectiveness of the proposed algorithms. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：2514 / 2527

页数：14

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