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Inferring directed networks using a rank-based connectivity measure.

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This study introduces a novel rank-based nonlinear interdependence measure to infer network topology and coupling direction. The method successfully reconstructs networks from coupled Lorenz dynamics and EEG data, even with added noise.

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Area of Science:

  • Complex systems analysis
  • Network science
  • Nonlinear dynamics

Background:

  • Understanding system interactions requires inferring network topology.
  • Existing methods like cross-correlation and mutual information fail to determine coupling direction.
  • Data-driven approaches are crucial for analyzing complex real-world systems.

Purpose of the Study:

  • To introduce a rank-based nonlinear interdependence measure for inferring network topology and coupling direction.
  • To evaluate the measure's effectiveness in reconstructing networks from simulated and real-world data.
  • To investigate the impact of dynamical noise on network reconstruction accuracy.

Main Methods:

  • Development and application of a rank-based nonlinear interdependence measure.
  • Testing the measure on a system of coupled Lorenz dynamics.
  • Analysis of multichannel electroencephalographic (EEG) recordings from an epilepsy patient.

Main Results:

  • The proposed measure successfully infers network topology and coupling direction for coupled Lorenz dynamics within a specific range of coupling strength and link density.
  • Dynamical noise was found to potentially benefit the network reconstruction process.
  • The method demonstrated applicability to real-world neurophysiological data (EEG).

Conclusions:

  • The rank-based nonlinear interdependence measure is a powerful tool for directed network inference in complex systems.
  • The method shows promise for analyzing biological systems, such as brain activity.
  • Further research can explore its application in various fields requiring network analysis.