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• W3037635596 abstract "Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude." @default.
• W3037635596 created "2020-07-02" @default.
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• W3037635596 date "2021-02-01" @default.
• W3037635596 modified "2023-10-07" @default.
• W3037635596 title "Large-scale influence maximization via maximal covering location" @default.
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• W3037635596 doi "https://doi.org/10.1016/j.ejor.2020.06.028" @default.
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