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• W3095137163 abstract "We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model citep{kempe2003maximizing}, this model's diffusion process is adversarially adapted to the choice of seed set. Hence, instead of optimizing under the assumption that all influence relationships in the network are independent, we seek a seed set whose expected influence under the worst correlation, i.e. the worst-case, expected influence, is maximized. We show that this worst-case influence can be efficiently computed, and though the optimization is NP-hard, a ($1 - 1/e$) approximation guarantee holds. We also analyze the structure to the adversary's choice of diffusion process, and contrast with established models. Beyond the key computational advantages, we also highlight the extent to which the independence assumption may cost optimality, and provide insights from numerical experiments comparing the adversarial and independent cascade model." @default.
• W3095137163 created "2020-11-09" @default.
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• W3095137163 date "2020-10-24" @default.
• W3095137163 modified "2023-09-27" @default.
• W3095137163 title "Correlation Robust Influence Maximization" @default.
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