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Given a social network G and an integer k, the influence maximization (IM) problem asks for a seed set S of k nodes from G to maximize the expected number of nodes influenced via a propagation model. The majority of the existing algorithms for the IM problem are developed only under the non-adaptive setting, i.e., where all k seed nodes are selected in one batch without observing how they influence other users in real world. In this paper, we study the adaptive IM problem where the k seed nodes are selected in batches of equal size b, such that the i-th batch is identified after the actual influence results of the former batches are observed. In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of , where and is a user-specified parameter. In particular, we propose a general …