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In this paper, we study a highly generic version of influence maximization (IM), one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of candidates, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. We call this problem online influence maximization with persistence. We introduce an estimator on the candidates'doi:10.1109/icdm.2017.118 dblp:conf/icdm/LagreeCCM17 fatcat:kn2raojfzzd6hng6mltvvkhbxu