Revealing physical interaction networks from statistics of collective dynamics

Mor Nitzan, Jose Casadiego, Marc Timme*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system’s model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems.

Original languageAmerican English
Article numbere1600396
JournalScience advances
Volume3
Issue number2
DOIs
StatePublished - Feb 2017

Bibliographical note

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2017 © The Authors, some rights reserved

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