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Related Experiment Video

Updated: Oct 15, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

Reconstructing network structures from partial measurements.

Melvyn Tyloo¹, Robin Delabays², Philippe Jacquod¹

¹Department of Quantum Matter Physics, University of Geneva, CH-1211 Geneva, Switzerland.

Chaos (Woodbury, N.Y.)

|October 31, 2021

Summary

This summary is machine-generated.

We reveal how to infer network structures within observed agents using velocity signal correlators. This method efficiently uncovers direct couplings and geodesic distances in dynamical systems, improving network inference.

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Area of Science:

Complex Systems
Network Science
Dynamical Systems

Background:

The coupling network structure dictates the dynamics of interacting agents.
Inferring network structure is crucial for control, prediction, and understanding inter-agent processes.
Existing methods require data from all network nodes, limiting their applicability.

Purpose of the Study:

To develop a method for inferring network structures solely from observed/measured agents.
To analyze the information encoded in velocity signal correlators for network inference.
To provide a more efficient alternative to traditional network inference techniques.

Main Methods:

Analytical derivation for symmetrically coupled dynamical systems near a stable equilibrium.
Numerical illustration of the proposed method.
Utilizing velocity signal correlators to extract network topology information.

Main Results:

Velocity signal correlators encode both direct couplings and geodesic distances within the observed subset of agents.
The proposed method is analytically established and numerically validated.
The approach offers algorithmic efficiency compared to traditional matrix inversion methods when all agent data is available.

Conclusions:

Network inference within observed agents is feasible using velocity signal correlators.
Geodesic distances in the coupling network can be inferred from local dynamics.
This work advances network inference by enabling analysis with partial observability and improved computational efficiency.