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  Revealing physical interaction networks from statistics of collective dynamics

Nitzan, M., Casadiego Bastidas, J. L., & Timme, M. (2017). Revealing physical interaction networks from statistics of collective dynamics. Science Advances, 3(2): e1600396. doi:10.1126/sciadv.1600396.

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 Creators:
Nitzan, M., Author
Casadiego Bastidas, José Luis1, Author           
Timme, Marc1, Author           
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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Free keywords: nonlinear dynamics, network dynamics, network reconstruction, physical connectivity, structural connectivity, gene networks
 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.

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Language(s): eng - English
 Dates: 2017-02-10
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1126/sciadv.1600396
 Degree: -

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Title: Science Advances
Source Genre: Journal
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Pages: 6 Volume / Issue: 3 (2) Sequence Number: e1600396 Start / End Page: - Identifier: -