title: Revealing physical interaction networks from statistics of collective dynamics.
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.
authors.affiliation: Racah Institute of Physics, Hebrew University of Jerusalem, 9190401 Jerusalem, Israel.; Faculty of Medicine, Hebrew University of Jerusalem, 9112001 Jerusalem, Israel.; School of Computer Science, Hebrew University of Jerusalem, 9190401 Jerusalem, Israel.
authors.forename: Mor
authors.initials: M
authors.lastname: Nitzan
authors.affiliation: Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.; International Max Planck Research School for Physics of Biological and Complex Systems, 37077 Göttingen, Germany.
authors.forename: Jose
authors.initials: J
authors.lastname: Casadiego
authors.affiliation: Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.; International Max Planck Research School for Physics of Biological and Complex Systems, 37077 Göttingen, Germany.; Technical University of Dresden, Institute for Theoretical Physics, 01062 Dresden, Germany.; Bernstein Center for Computational Neuroscience, 37077 Göttingen, Germany.; Department of Physics, Technical University of Darmstadt, 64289 Darmstadt, Germany.
authors.forename: Marc
authors.initials: M
authors.lastname: Timme
keywords: gene networks; network dynamics; network reconstruction; nonlinear dynamics; physical connectivity; structural connectivity