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In this paper we consider the problem of maximizing information propagation in social networks. To solve it, we introduce a probabilistic maximum coverage problem, and further purpose a cluster-based heuristic and a neighborhoodremoval heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Our proposed strategies are compared with the pure greedy algorithm and centrality-based schemes via experiments on large collaborationdoi:10.1109/glocom.2011.6133985 dblp:conf/globecom/FanL11a fatcat:zi6htgdvzrfljbx2fblemtpuh4