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Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks.

Shuo Wang1, Erik D Herzog2, István Z Kiss3

  • 1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.

Proceedings of the National Academy of Sciences of the United States of America
|August 29, 2018
PubMed
Summary
This summary is machine-generated.

We developed a unified, data-driven method to efficiently infer network connections (ICON). This approach reliably reveals complex network topologies in diverse systems, from chemical oscillators to social behaviors in mice.

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Area of Science:

  • Complex systems science
  • Network science
  • Computational neuroscience

Background:

  • Understanding dynamic network topologies is crucial for complex systems like biological and financial networks.
  • Existing methods struggle with the scale and nonlinearity of modern systems (e.g., brain, social networks).

Purpose of the Study:

  • To develop a unified, data-driven approach for efficient inference of network connections.
  • To address the challenges of scale and nonlinearity in network topology estimation.

Main Methods:

  • Developed the Inferring Connections of Networks (ICON) method.
  • Formulated large-scale, nonlinear estimation problems as linear inverse problems.
  • Utilized parallel computing for efficient problem-solving.

Main Results:

  • Successfully applied ICON to diverse networks: oscillators, electrochemical systems, neuronal networks, and mouse social groups.
  • Demonstrated ICON's robustness and versatility in revealing network topologies.
  • Identified full and partial resonance in chemical oscillators and coherent circadian rhythms in cells.

Conclusions:

  • ICON provides an efficient and reliable method for inferring complex network topologies.
  • The approach is applicable to a wide range of nonlinear systems across different scientific domains.
  • Facilitates understanding of functional connectivity and synchronization in large-scale networks.