Adaptive seeding for profit maximization in social networks

Social networks are becoming important dissemination platforms, and a large body of works have been performed on viral marketing, but most are to maximize the benefits associated with the number of active nodes. In this paper, we study the benefits related to interactions among activated nodes. Furthermore, since the stochasticity caused by the dynamics of influence cascade in the social network, we propose the adaptive seeding strategy where seeds are selected one by one according to influence propagation situation of seeds already selected, and define the adaptive profit maximization problem. We analyze its complexity and prove it is not adaptive submodular. We find the upper and lower bounds which are adaptive submodular and design an adaptive sandwich policy based on the sandwich strategy which could gain a data dependent approximation solution. Through real data sets, we verify the effectiveness of our proposed algorithm.