Scholarlink

<p>This paper investigates how to optimize the infection rate and public opinion of a large population during a viral pandemic to minimize social costs. Due to the high dimensionality of the network, the computational burden is typically significant. Therefore, this paper proposes an optimization solution strategy based on clustering analysis. By approximating the clustering of the entire network, an optimization problem on the quotient graph is obtained. Theoretical analysis shows that the network optimization problem on the quotient graph can approximate the original optimization problem. For the optimization problem on the quotient graph, the corresponding Hamilton–Jacobi–Bellman (HJB) equation is derived. A single-critic neural network algorithm is employed to approximate the solution of the HJB equation and determine the optimal control law. Finally, real-world data on COVID-19 is used to illustrate the effectiveness of the obtained results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cluster analysis-driven optimization for infection reduction and awareness raising in virus-opinion coupled networks

  • 期刊论文

  • 发表日期：

    2025年4月28日

查看全文
  • Fang Zhang,
  • Shidong Zhai,
  • Jiyu Zhang,
  • Junli Tao

摘要

This paper investigates how to optimize the infection rate and public opinion of a large population during a viral pandemic to minimize social costs. Due to the high dimensionality of the network, the computational burden is typically significant. Therefore, this paper proposes an optimization solution strategy based on clustering analysis. By approximating the clustering of the entire network, an optimization problem on the quotient graph is obtained. Theoretical analysis shows that the network optimization problem on the quotient graph can approximate the original optimization problem. For the optimization problem on the quotient graph, the corresponding Hamilton–Jacobi–Bellman (HJB) equation is derived. A single-critic neural network algorithm is employed to approximate the solution of the HJB equation and determine the optimal control law. Finally, real-world data on COVID-19 is used to illustrate the effectiveness of the obtained results.