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Parameter estimation in interacting particle systems on dynamic random networks.

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Summary
This summary is machine-generated.

This study introduces a method to infer the dynamics of particle systems on evolving networks. The approach uses partial data, specifically edge counts, to understand system behavior effectively.

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Area of Science:

  • Complex Systems
  • Network Science
  • Statistical Physics

Background:

  • Interacting particle systems are fundamental in various scientific domains.
  • Dynamic random networks exhibit complex evolving structures.
  • Understanding system dynamics from partial observations is a significant challenge.

Purpose of the Study:

  • To develop an inference method for particle systems on dynamic random networks.
  • To estimate underlying system dynamics using limited observational data.
  • To validate the proposed inference technique through numerical simulations.

Main Methods:

  • Modeling particle systems with one-way feedback between vertex and edge dynamics.
  • Utilizing snapshots of the total number of edges as partial information.
  • Employing statistical inference techniques to estimate system parameters.

Main Results:

  • Demonstrated the ability to infer system dynamics from edge count data.
  • Numerical results confirm the effectiveness of the proposed inference method.
  • The method successfully captures the behavior of the interacting particle system.

Conclusions:

  • The developed inference method is effective for dynamic random networks.
  • Partial information, like edge counts, can be sufficient for system analysis.
  • This work provides a valuable tool for studying complex interacting systems.