Robust Profit Maximization with Double Sandwich Algorithms 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, due to the uncertainty in edge probability estimates in social networks, we propose the robust profit maximization problem to have the best solution in the worst case of probability settings. We design a double sandwich algorithm to this problem and further improve the algorithm with sampling method such that it increases robustness of the output. Through real data sets, we verify the effectiveness of our proposed algorithm.