Network reconstruction from random phase-resetting
arXiv:1012.3624 · doi:10.1103/PhysRevLett.107.034101
Abstract
We propose a novel method of reconstructing the topology and interaction functions for a general oscillator network. An ensemble of initial phases and the corresponding instantaneous frequencies is constructed by repeating random phase-resets of the system dynamics. The desired details of network structure are then revealed by appropriately averaging over the ensemble. The method is applicable for a wide class of networks with arbitrary emergent dynamics, including full synchrony.
References in corpus (9)
- Community detection in graphs
- Synchronization in complex networks
- Revealing Network Connectivity From Dynamics
- Inferring Network Topology from Complex Dynamics
- Noise bridges dynamical correlation and topology in coupled oscillator networks
- Mean Field Theory For Non-Equilibrium Network Reconstruction
- Phase resetting of collective rhythm in ensembles of oscillators
- Phase Response Curves of Coupled Oscillators
- Stability and chaos in coupled two-dimensional maps on Gene Regulatory Network of bacterium E.Coli
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