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Area of Science:

  • Complex Systems
  • Network Science
  • Dynamical Systems

Background:

  • Inferring causal relationships in networked dynamical systems from data is a significant challenge.
  • Statistical methods like Granger causality are widely used for data-driven network discovery.

Purpose of the Study:

  • To systematically evaluate the accuracy of pairwise-conditional Granger causality for inferring network structure.
  • To compare inferred network connectivity with known ground truth structures.

Main Methods:

  • Simulated networked systems of Kuramoto oscillators with known causal structures.
  • Applied the Multivariate Granger Causality Toolbox for network inference.
  • Compared inferred networks against ground truth across various parameters.

Main Results:

  • Significant systematic disparities were found between inferred and true network structures, except for extremely sparse or dense networks.
  • Inferred networks showed discrepancies in edge count and connectivity matrix eigenvalues, leading to inconsistent dynamics.
  • Detailed analysis provided for the Erdős-Rényi network model.

Conclusions:

  • Granger causality methods for network inference are highly suspect and require validation against ground truth models.
  • Network inference methods should be rigorously compared with ground truth systems to ensure reliability.