Reconstructing Links in Directed Networks from Noisy Dynamics
arXiv:1604.02224 · doi:10.1103/PhysRevE.95.010301
Abstract
In this Letter, we address the longstanding challenge of how to reconstruct links in directed networks from measurements, and present a general method that makes use of a noise-induced relation between network structure and both the time-lagged covariance of measurements taken at two different times and the covariance of measurements taken at the same time. For coupling functions that have additional properties, we can further reconstruct the weights of the links.
References in corpus (7)
- Scale-free brain functional networks
- Revealing Network Connectivity From Dynamics
- Noise bridges dynamical correlation and topology in coupled oscillator networks
- Revealing networks from dynamics: an introduction
- Solving the inverse problem of noise-driven dynamic networks
- Does dynamics reflect topology in directed networks?
- Reconstruction of chaotic neural network from observed firing rates
Cited by in corpus (15)
- Model-free inference of direct network interactions from nonlinear collective dynamics
- Estimating the impact of structural directionality: How reliable are undirected connectomes?
- Deriving pairwise transfer entropy from network structure and motifs
- Local Tomography of Large Networks under the Low-Observability Regime
- Effects of hidden nodes on the reconstruction of bidirectional networks
- Revealing directed effective connectivity of cortical neuronal networks from measurements
- Large order fluctuations, switching, and control in complex networks
- Detecting Directed Interactions of Networks by Random Variable Resetting
- Transition to Reconstructibility in Weakly Coupled Networks
- Impact of lag information on network inference
- Reconstructing dynamics of complex systems from noisy time series with hidden variables
- Reconstructing Network Structures from Partial Measurements
- Optimized two-dimensional Networks with edge crossing cost: frustrated anti-ferromagnetic spin system
- Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
- Topology Inference over Networks with Nonlinear Coupling