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• W2976691036 abstract "We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible. We formulate the problem as a repeated game between a player and adversary, where the adversary specifies the edges along which the contagion may spread, and the player chooses sets of nodes to influence in an online fashion. We establish upper and lower bounds on the minimax pseudo-regret in both undirected and directed networks." @default.
• W2976691036 created "2019-10-03" @default.
• W2976691036 creator A5021502006 @default.
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• W2976691036 date "2019-07-01" @default.
• W2976691036 modified "2023-09-25" @default.
• W2976691036 title "Adversarial Influence Maximization" @default.
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• W2976691036 doi "https://doi.org/10.1109/isit.2019.8849828" @default.
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