Methods for selecting nodes for maximal spread of influence in recommendation services

Abstract

Social network analysis is a tool to assess social interactions between people e.g. in the Internet. One of the most active areas in this field are modeling influence of users and finding influential users. These areas have many applications, e.g., in marketing, business or politics. Several models of influence have been described in literature, but there is no single model that best describes the process of spreading entities (e.g. information, behaviour) through the network. Interesting and practical problem is how to choose a small number of users that will guarantee maximal spread of entities over the whole network (influence maximization problem). In this paper we studied this problem using various centrality metrics with different models of influence propagation. Experiments were conducted on three, real-world datasets regarding the domain of recommendation services.