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Related Experiment Video

Updated: May 18, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

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Forecasting synchronizability of complex networks from data.

Ri-Qi Su¹, Xuan Ni, Wen-Xu Wang

¹School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, 85287, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

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Summary

This summary is machine-generated.

Researchers can now predict and control synchronization in complex networks using only time-series data. This method reconstructs network topology, coupling, and dynamics, enabling forecasting of collective behaviors.

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Area of Science:

Complex Systems Science
Network Science
Nonlinear Dynamics

Background:

Understanding collective dynamics in unknown complex networked systems is challenging.
Synchronization is a key type of collective behavior in coupled dynamical networks.
Existing methods often require full knowledge of network topology and dynamics.

Purpose of the Study:

To determine if collective dynamics, specifically synchronization, can be predicted from time-series data alone in unknown complex networks.
To develop a method for reconstructing network properties and dynamics from limited measurements.

Main Methods:

Utilized the compressive-sensing paradigm to reconstruct unknown network properties.
Accurately recovered full network topology, non-uniform coupling weights, and nodal dynamical equations.
Analyzed reconstruction accuracy and data requirements, validating with the reconstructed eigenvalue spectrum.

Main Results:

Successfully reconstructed weighted network topology and dynamics from time-series data.
Calculated the Master Stability Function (MSF) from the reconstructed system.
Demonstrated accurate prediction and potential control of synchronous dynamics.

Conclusions:

It is possible to foresee and control collective dynamics like synchronization in complex networks using only time-series measurements.
The compressive-sensing approach provides a powerful tool for analyzing unknown networked systems.
This work is a foundational step towards forecasting collective dynamics on complex networks.