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A set covering greedy algorithm is proposed for solving the influence maximization problem in social networks. Two information diffusion models are considered: Independent Cascade Model and Linear Threshold Model. The proposed algorithm is compared with traditional maximization algorithms such as simple greedy and degree centrality using three data sets. In addition, an algorithm for mapping social networks is proposed, which allows visualizing the infection process and how the different algorithms evolve. The proposed approach is useful for mining large social networks.