Near optimal strategies for targeted marketing in social networks
In this paper, we address the problem of Targeted Influence Maximization (TIM) through a social network. Often companies want to promote their products to certain type of customers as opposed to targeting the entire social network. That is, there is a need to maximize influence over a targeted audience in the network. Towards this end, we present a novel objective function for the targeted influence maximization problem. It turns out that this objective function is the difference between two relevant submodular functions. By building upon the recently developed theory for optimizing the difference between two submodular functions, we develop an efficient algorithm with provable guarantees. We show that the quality of solution for TIM improves using our proposed approach, when compared over a standard baseline.