Influence Maximization Problem in Social Networks: An Overview

Abstract

Social networks have attracted a great deal of attention and have in fact important information vectors that have changed the way we produce, consume and diffuse information. Social networks' analysis has been of great interest and has encompassed different research areas including community detection, the discovery of web services from social networks, information diffusion, detection of infuential nodes. The process of detecting influential nodes in social networks is often khown as Influence Maximization (IM) problem, it deals with finding a small subset of nodes that spread maximum influence in the network. It has been proved that it has many applications such as the propagation of opinions, the study of the acceptance of political blogs or the study of the degree of adhesion of an actor to a product in marketing (web marketing). A such maximization requieres the presence of a diffusion model that controls information propagation within active individuals. This paper aims to provide a survey on the influence maximization problem and focuses on two aspects, influence diffusion models and proposed approaches for influential nodes detection. We start by describing formally the IM problem, then we will provide the state-of-the-art of both diffusion models and influence maximization algorithms.