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.

arXiv comment7 pages, 5 figures

References in corpus (11)

• Measuring Information Transfer
• Weak pairwise correlations imply strongly correlated network states in a neural population
• Analysis of a power grid using the Kuramoto-like model
• Predicting catastrophes in nonlinear dynamical systems by compressive sensing
• Revealing Network Connectivity From Dynamics
• Inferring Network Topology from Complex Dynamics
• Dynamic information routing in complex networks
• Revealing networks from dynamics: an introduction
• Coexistence of regular and irregular dynamics in complex networks of pulse-coupled oscillators
• Resiliently evolving supply-demand networks
• Fair sharing of resources in a supply network with constraints

Cited by in corpus (22)

• Model-free inference of direct network interactions from nonlinear collective dynamics
• Network reconstruction and community detection from dynamics
• Detecting Hidden Units and Network Size from Perceptible Dynamics
• Inferring network connectivity from event timing patterns
• Blind identification of stochastic block models from dynamical observations
• Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data
• Reconstructing dynamical networks via feature ranking
• Measuring topological descriptors of complex networks under uncertainty
• Physically-interpretable classification of biological network dynamics for complex collective motions
• Modes of Information Flow in Collective Cohesion
• "Layer-by-layer" Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical Systems
• Revealing dynamics, communities and criticality from data
• From State Estimation to Network Reconstruction
• Command of Collective Dynamics by Topological Defects in Spherical Crystals
• Synchronization Invariance Under Network Structural Transformations
• A practical method for estimating coupling functions in complex dynamical systems
• Transition to Reconstructibility in Weakly Coupled Networks
• Decoupling approximation robustly reconstructs directed dynamical networks
• KuraNet: Systems of Coupled Oscillators that Learn to Synchronize
• Network Reconstruction Problem for an Epidemic Reaction-Diffusion
• Data based reconstruction of complex multiplex networks
• Topology Inference over Networks with Nonlinear Coupling