Influence Maximization in Social Networks

Abstract

Influence maximization (IM) is the problem of identifying a small subset of influential users such that influence spread in a network can be maximized. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing, election campaign, counter-terrorism efforts, rumor control, and sales promotions, etc. In this paper, we perform a comparative review of the existing IM algorithms. First, we present a comprehensive study on existing IM approaches with their comparative theoretical analysis. Then, we present a comparative analysis of existing IM methods with respect to performance metrics. Finally, we discuss the challenges and future directions of the research. © Springer Nature Switzerland AG 2019.