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Area of Science:

  • Complex systems analysis
  • Network science
  • Data-driven modeling

Background:

  • Analyzing large datasets from natural and social systems is essential.
  • Inverse problems, inferring system structure from data, are critical in interdisciplinary research.
  • Dynamic complex networks present unique analytical challenges.

Purpose of the Study:

  • To address the inverse problem for dynamic complex networks driven by white noise.
  • To develop a universal and accurate method for inferring network structures and noise correlations.
  • To demonstrate the feasibility of precise inference from output data alone.

Main Methods:

  • Analytical derivation of a novel inference formula.
  • Development of the double correlation matrices and noise-decorrelation (DCMND) method.
  • Validation through numerical simulations.

Main Results:

  • The DCMND method accurately depicts dynamic network structures.
  • The method successfully identifies noise correlations within the systems.
  • Inference performance surpasses previously achievable theoretical and computational limits.

Conclusions:

  • The DCMND method offers a powerful tool for understanding complex dynamic networks.
  • Accurate inference of network structure and noise is achievable using only output data.
  • This work advances the field of inverse problem-solving in complex systems analysis.