A fast algorithm for finding most influential people based on the linear threshold model. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Rahimkhani, Khadije; Aleahmad, Abolfazl; Rahgozar, Maseud; Moeini, Ali
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1353
• Page End: 1361
• Publication Date: 2015-02-15
• Subjects: Social networks -- Influential people retrieval -- Influence maximization -- Linear threshold model; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.037
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new algorithm, named ComPath, is proposed based on the linear threshold model. We make use of structural communities of the input network. Experimental evaluations are carried out by use of NetHEPT and Epinion datasets. ComPath is faster and more efficient than the state of the art algorithms. Abstract: 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.