A fast algorithm for finding most influential people based on the linear threshold model

被引：87

作者：

Rahimkhani, Khadije ^{[1
]}

Aleahmad, Abolfazl ^{[1
]}

Rahgozar, Maseud ^{[1
]}

Moeini, Ali ^{[2
]}

机构：

[1] Univ Tehran, Control & Intelligent Proc Ctr Excellence, Sch Elect & Comp Engn, Database Res Grp, Tehran 14174, Iran

[2] Univ Tehran, Fac Engn Sci, Sch Engn, Tehran 14174, Iran

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2015年 / 42卷 / 03期

关键词：

Social networks; Influential people retrieval; Influence maximization; Linear threshold model; INFLUENCE MAXIMIZATION; COMPLEXITY;

D O I：

10.1016/j.eswa.2014.09.037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Finding the most influential people is an NP-hard problem that has attracted many researchers in the field of social networks. The problem is also known as influence maximization and aims to find a number of people that are able to maximize the spread of influence through a target social network. In this paper, a new algorithm based on the linear threshold model of influence maximization is proposed. The main benefit of the algorithm is that it reduces the number of investigated nodes without loss of quality to decrease its execution time. Our experimental results based on two well-known datasets show that the proposed algorithm is much faster and at the same time more efficient than the state of the art algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：1353 / 1361

页数：9