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Towards Digital Twin-Oriented Complex Networked Systems: Introducing heterogeneous node features and interaction

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This study introduces a Digital Twin-Oriented Complex Networked Systems (DT-CNSs) framework to model social networks. Findings show targeted interventions on high-risk nodes enhance disaster resilience during epidemics.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Social Science

Background:

  • Digital Twin-Oriented Complex Networked Systems (DT-CNSs) require robust modeling frameworks.
  • Real-world social networks exhibit complex features and interaction rules influencing system dynamics.

Purpose of the Study:

  • To propose an extendable modeling framework for DT-CNSs.
  • To generate networks that faithfully represent real-world social systems.
  • To investigate disaster resilience in social networks during epidemic outbreaks.

Main Methods:

  • Developed a modeling framework focusing on node features and interaction rules.
  • Conducted simulation-based experiments on DT-CNSs with varying complexity.
  • Analyzed epidemic spread and infection occurrence within social networks.

Main Results:

  • Structural and dynamic complexities influence network growth and epidemic spread.
  • Nodes with preferred features exhibit higher infection risks.
  • Mitigation policies targeting high-risk nodes are crucial for disaster resilience.

Conclusions:

  • The proposed DT-CNSs framework effectively models social networks.
  • Understanding node features and interaction rules is key to network resilience.
  • Targeted interventions are essential for effective epidemic control and disaster management.