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• W4297168412 abstract "Information propagation on networks is a central theme in social, behavioral, and economic sciences, with important theoretical and practical implications, such as the influence maximization problem for viral marketing. Here, we consider a model that unifies the classical independent cascade models and the linear threshold models, and generalise them by considering continuous variables and allowing feedback in the dynamics. We then formulate its influence maximization as a mixed integer nonlinear programming problem and adopt derivative-free methods. Furthermore, we show that the problem can be exactly solved in the special case of linear dynamics, where the selection criterion is closely related to the Katz centrality, and propose a customized direct search method with local convergence. We then demonstrate the close-to-optimal performance of the customized direct search numerically on both synthetic and real networks." @default.
• W4297168412 created "2022-09-27" @default.
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• W4297168412 date "2022-09-16" @default.
• W4297168412 modified "2023-10-17" @default.
• W4297168412 title "Unifying information propagation models on networks and influence maximization" @default.
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• W4297168412 doi "https://doi.org/10.1103/physreve.106.034316" @default.
• W4297168412 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36266854" @default.
• W4297168412 hasPublicationYear "2022" @default.
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