Detecting Directional Coupling in Network Dynamical Systems via Kalman’s Observability

Detecting coupling in network dynamical systems from time series is an open problem in the physics of complex systems. In this Letter, we tackle this issue from a control-theoretic perspective. Drawing inspiration from Kalman's notion of observability, we argue the presence of directional coupling between two units, X→Y, when X is detected as an internal state from the measurement of Y. We illustrate this approach on a series of analytically tractable systems, showcasing how it overcomes limitations of state-of-the-art methods for network inference.

Authors

Institutions

Publication Details

Journal
Physical Review Letters
Published
2025-02-18
DOI
https://doi.org/10.1103/physrevlett.134.077401
Citations
1
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
1.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detecting Directional Coupling in Network Dynamical Systems via Kalman’s Observability

Maurizio Porfiri, Rayan Succar
1 citations
Physical Review Letters
Neural dynamics and brain function
1.43
article

Detecting Directional Coupling in Network Dynamical Systems via Kalman’s Observability

Maurizio Porfiri, Rayan Succar
article en
1 citations

Abstract

Detecting coupling in network dynamical systems from time series is an open problem in the physics of complex systems. In this Letter, we tackle this issue from a control-theoretic perspective. Drawing inspiration from Kalman's notion of observability, we argue the presence of directional coupling between two units, X→Y, when X is detected as an internal state from the measurement of Y. We illustrate this approach on a series of analytically tractable systems, showcasing how it overcomes limitations of state-of-the-art methods for network inference.

Physical Review LettersVol. 134(7)
City University of New York (US), New York University (US)
National Science Foundation
Affordable and clean energy
Openalex Percentile: Top 22%
Neural dynamics and brain function
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.