Adaptive influence maximization under fixed observation time-step

The influence maximization problem aims to find some seeds which can cause the maximum influence spread results in a social network. Most researches focus on the non-adaptive strategies, in which all seeds are selected at once. For the non-adaptive strategies, the seeds may influence other seeds in the influence spread process and make the waste of budget. This paper considers the adaptive strategies and studies the adaptive influence maximization and adaptive stochastic influence maximization in the general feedback model. These problems select seeds adaptively, and it completes each selection after the fixed observation time-step. In this paper, we utilize the adaptive greedy to solve these problems and propose a theoretical analysis by introducing a comparative factor. In addition, we present the feasible approximation algorithm using the reverse sampling technique. Finally, we carry out experiments on three networks to show the efficiency of adaptive strategies.