Minimizing the Influence Propagation in Social Networks for Linear Threshold Models

被引：23

作者：

Yang, Lan ^{[1
,2
]}

Giua, Alessandro ^{[2
,4
]}

Li, Zhiwu ^{[1
,3
]}

机构：

[1] Xidian Univ, SEME, Xian 710071, Shaanxi, Peoples R China

[2] Aix Marseille Univ, Univ Toulon, CNRS, ENSAM,LSIS, Marseille, France

[3] Macau Univ Sci & Technol, ISE, Taipa, Macao, Peoples R China

[4] Univ Cagliari, DIEE, Cagliari, Italy

来源：

IFAC PAPERSONLINE | 2017年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Social network; Optimization; Influence propagation; Linear Threshold model;

D O I：

10.1016/j.ifacol.2017.08.2293

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

Innovation or information propagation in social networks has been widely studied in recent years. Most of the previous works are focused on solving the problem of influence maximization, which aims to identify a small subset of early adopters in a social network to maximize the influence propagation under a given diffusion model. In this paper, motivated by practical scenarios, we propose two different influence minimization problems. We consider a Linear Threshold diffusion model and provide a general solution to the first problem solving a linear integer programming. For the second problem, we provide a technique to search for an optimal solution that works only in particular cases and discuss a simple heuristic to find a solution in the general case. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：14465 / 14470

页数：6

共 16 条

[1]

Acemoglu D, 2011, IEEE DECIS CONTR P, P2329, DOI 10.1109/CDC.2011.6160999

[2]

[Anonymous], 2011, P 20 INT C WORLD WID, DOI [10.1145/1963405.1963499, DOI 10.1145/1963405.1963499]

[3]

[Anonymous], 2001, P 7 ACM SIGKDD INT C, DOI DOI 10.1145/502512.502525

[4]

The anatomy of a large-scale hypertextual Web search engine [J].

Brin, S ;

Page, L .

COMPUTER NETWORKS AND ISDN SYSTEMS, 1998, 30 (1-7) :107-117

[5]

Efficient Influence Maximization in Social Networks [J].

Chen, Wei ;

Wang, Yajun ;

Yang, Siyu .

KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2009, :199-207

[6]

Cheng-Hsin Weng, 2010, Proceedings of the 2010 5th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS 2010), P14, DOI 10.1109/NEMS.2010.5592127

[7]

Goyal A., 2011, Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM 2011), P211, DOI 10.1109/ICDM.2011.132

[8]

Kempe D., 2003, P 9 ACM SIGKDD INT C, P137, DOI DOI 10.1145/956750.956769

[9]

Leskovec J, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P420

[10]

IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks [J].

Liu, Xiaodong ;

Li, Mo ;

Li, Shanshan ;

Peng, Shaoliang ;

Liao, Xiangke ;

Lu, Xiaopei .

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2014, 25 (01) :136-145

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