Mean-field control and deep learning for information dissemination problem in online social networks
Year: 2025
Type: conference-paper
Abstract: The paper formulates and numerically investigates the tasks of stopping and stimulating the dissemination of information in online social networks for a mean-field model and a model based on artificial neural networks.The mean field model is reduced to a joint solution of initial boundary value problems for the Kolmogorov-Fokker-Planck (KFP) and Hamilton-Jacobi-Bellman (HJB) equations, as well as the Nash optimality condition.Numerical methods for solving systems of KFP and HJB in high dimensions are practically nonexistent due to the need for grid-based spatial discretization.Therefore, we propose a deep learning algorithm based on the generative adversarial network.We parameterize the value (the solution of HJB) and density (the solution of KFP) functions by two neural networks that solve a convex-concave saddle-point problem.
DOI: 10.51408/csit2025_71
Institution: Sobolev Institute of Mathematics
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4414454103
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