A Novel Triangle Count-Based Influence Maximization Method on Social Networks

Abstract

Influence maximization aims to identify a small set of influential individuals in a social network capable of spreading influence to the most users. This problem has received wide attention due to its practical applications, such as viral marketing and recommendation systems. However, most of the existing methods ignore the presence of community structure in networks, and many of the recently proposed community-based methods are ineffective on all types of networks. In this paper, the authors propose a method called the triangle influence seed selection approach (TISSA) for finding k influential nodes based on the counting triangles in the network. The approach focuses primarily on identifying structurally coherent nodes to find influential nodes without applying community detection algorithms. The results on real-world and synthetic networks illustrate that the proposed method is more effective on networks with community structures in producing the highest influence spread and more time-efficient than the state-of-the-art algorithms.