User Profiling and Influence Maximization

Abstract

For nearly a half-century, influence maximization has been a popular topic in computational social network analysis. However, identifying k nodes “users” among all the nodes in a directed network in such a way that activating them results in the highest predicted number of activated nodes is a problem known as influence maximization. Its actual importance appears in various fields, such as targeted advertising, viral marketing, personalized recommendation, and so on. For many years, the subject of influence maximization (IM) has been addressed, and various solutions have been presented. Many social strategies assist marketers in profiling a small and specialized set of consumers in order to advertise their products and increase their influence spread, this group is called influencers. This study could be viewed as a literature review on influence maximization topic with user profiling. The objective of this research is to present the influence maximizing, to provide a global vision of IM over big data era, and demonstrate how to identify influencers from social networks with an application.