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Belief and opinion evolution in social networks (SNs) can aid in understanding how people influence others’ decisions through social relationships. As a large number of users are involved in SNs, the complexity of traditional optimization techniques is high as they deal with the interactions between users separately. Moreover, the state variable (opinion) is high-dimensional because a person usually has opinions about many different social issues. Incorporating classical opinion dynamics, we formulate the opinion evolution in SNs as a high-dimensional stochastic mean field game (MFG).

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Opinion Evolution in Social Networks: Use Generative Adversarial Networks to Solve Mean Field Game

  • 章节

  • 发表日期：

    2025年

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  • Yuhan Kang,
  • Hao Gao,
  • Zhu Han

摘要

Belief and opinion evolution in social networks (SNs) can aid in understanding how people influence others’ decisions through social relationships. As a large number of users are involved in SNs, the complexity of traditional optimization techniques is high as they deal with the interactions between users separately. Moreover, the state variable (opinion) is high-dimensional because a person usually has opinions about many different social issues. Incorporating classical opinion dynamics, we formulate the opinion evolution in SNs as a high-dimensional stochastic mean field game (MFG).