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Improving on transfer entropy-based network reconstruction using time-delays: Approach and validation.

Rifat Sipahi1, Maurizio Porfiri2

  • 1Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA.

Chaos (Woodbury, N.Y.)
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PubMed
Summary
This summary is machine-generated.

This study introduces time-delayed transfer entropy to improve network reconstruction by distinguishing direct from indirect interactions. This method enhances causal inference in dynamical systems and network analysis.

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Area of Science:

  • Complex systems
  • Information theory
  • Network science

Background:

  • Transfer entropy is a model-free method for inferring causality from time-series data.
  • Existing methods struggle to differentiate direct from indirect interactions in network reconstruction due to transfer entropy's dyadic nature.

Purpose of the Study:

  • To develop a robust framework for network reconstruction by addressing the limitations of traditional transfer entropy.
  • To integrate time-delays into transfer entropy calculations to better capture network dynamics.

Main Methods:

  • Incorporating time-delays into transfer entropy computation to account for finite information transfer speed.
  • Relating time-delayed transfer entropy values to the number of walks between network nodes.
  • Developing a novel framework for network reconstruction based on these principles.

Main Results:

  • Demonstrated closed-form results for three-node networks.
  • Numerically validated the approach on larger networks, including Boolean models and chaotic maps.
  • Established a connection between time-delayed transfer entropy and network topology.

Conclusions:

  • Time-delayed transfer entropy offers a more robust method for network reconstruction.
  • This approach effectively distinguishes direct from indirect interactions in complex systems.
  • The framework provides a foundation for advanced causal inference in network analysis.