Maximizing social influence in real-world networks—The state of the art and current challenges

Abstract

The following chapter aims to present the current research in the area of modelling and maximizing social influence in networks. Apart from describing the most popular models for this process, it focuses on presenting the advances in maximizing the spread of influence in social networks. Since most of the research was suited for static networks case, nowadays it is necessary to move it toward the networks that are everywhere around us—the dynamic ones. As is widely agreed in the scientific community, static networks are unacceptable simplification of the real world processes, so current research is moving toward the temporal networks. It is especially important when modelling propagation phenomena, such as the spread of influence, epidemics or diffusion of innovations. In this chapter it is presented how the research on maximizing the spread of influence is starting to explore real-world cases and how the early attempts of solving this problem for temporal networks look like. Moreover, it is shown how to benefit from the temporal properties of the social network in order to achieve better results for spread of influence compared to the static approach.