Reconstructing Network Structures from Partial Measurements
arXiv:2007.16136 · doi:10.1063/5.0058739
Abstract
The dynamics of systems of interacting agents is determined by the structure of their coupling network. The knowledge of the latter is, therefore, highly desirable, for instance, to develop efficient control schemes, to accurately predict the dynamics, or to better understand inter-agent processes. In many important and interesting situations, the network structure is not known, however, and previous investigations have shown how it may be inferred from complete measurement time series on each and every agent. These methods implicitly presuppose that, even though the network is not known, all its nodes are. Here, we investigate the different problem of inferring network structures within the observed/measured agents. For symmetrically coupled dynamical systems close to a stable equilibrium, we establish analytically and illustrate numerically that velocity signal correlators encode not only direct couplings, but also geodesic distances in the coupling network within the subset of measurable agents. When dynamical data are accessible for all agents, our method is furthermore algorithmically more efficient than the traditional ones because it does not rely on matrix inversion.
arXiv comment10 pages, 5 figures
References in corpus (26)
- The structure and function of complex networks
- Synchronization in Complex Oscillator Networks and Smart Grids
- Reaction-diffusion processes and metapopulation models in heterogeneous networks
- Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback
- Revealing Network Connectivity From Dynamics
- Fundamental structures of dynamic social networks
- Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
- Network structure from rich but noisy data
- Inferring Network Topology from Complex Dynamics
- Dynamic information routing in complex networks
- Noise bridges dynamical correlation and topology in coupled oscillator networks
- Network reconstruction and community detection from dynamics
- The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities
- Robustness of Synchrony in Complex Networks and Generalized Kirchhoff Indices
- Reconstructing Links in Directed Networks from Noisy Dynamics
- The Size of the Sync Basin Revisited
- Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
- Optimal noise-canceling networks
- Multistability of Phase-Locking in Equal-Frequency Kuramoto Models on Planar Graphs
- Model reconstruction from temporal data for coupled oscillator networks
- Noise-Induced Desynchronization and Stochastic Escape from Equilibrium in Complex Networks
- Reverse engineering of complex dynamical networks in the presence of time-delayed interactions based on noisy time series
- Data-driven inference of hidden nodes in networks
- Revealing dynamics, communities and criticality from data
- From State Estimation to Network Reconstruction
- System Size Identification from Sinusoidal Probing in Diffusive Complex Networks