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Hybrid multiscale coarse-graining for dynamics on complex networks.

Chuansheng Shen1, Hanshuang Chen2, Zhonghuai Hou3

  • 1School of Mathematics and Computational Science, Anqing Normal University, Anqing 246011, China.

Chaos (Woodbury, N.Y.)
|January 3, 2019
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Summary
This summary is machine-generated.

We introduce a hybrid multiscale coarse-grained method combining detailed Monte Carlo simulations with coarse Langevin dynamics. This approach accurately models phase transitions and critical phenomena while significantly reducing computational cost for complex networks.

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Area of Science:

  • Computational Physics
  • Network Science
  • Statistical Mechanics

Background:

  • Simulating large-scale networked systems is computationally intensive.
  • Existing methods often struggle to balance accuracy and efficiency.
  • Multiscale modeling offers a potential solution for complex systems.

Purpose of the Study:

  • To develop an efficient hybrid multiscale coarse-grained (HMCG) method.
  • To validate the HMCG method on established models.
  • To demonstrate computational savings and accuracy in analyzing networked systems.

Main Methods:

  • Combining fine-grained Monte Carlo (MC) simulation for critical regions.
  • Employing coarse-grained Langevin dynamics for less critical regions.
  • Applying the HMCG method to the equilibrium Ising model and susceptible-infected-susceptible (SIS) model.

Main Results:

  • The HMCG method accurately reproduces phase transitions and critical phenomena.
  • Significant acceleration of dynamic evaluations compared to purely microscopic MC simulations.
  • Demonstrated substantial computational savings for whole network analysis.

Conclusions:

  • The HMCG method is a valid and efficient approach for simulating networked systems.
  • The method offers significant computational advantages without sacrificing accuracy.
  • HMCG is generalizable to various networked systems with appropriate parameterization.