Autonomous inference of complex network dynamics from incomplete and noisy data

Abstract

The availability of empirical data that capture the structure and behavior of complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social, and coupled oscillators dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to inferring the early spreading dynamics of H1N1 flu upon the worldwide airline network, and the inferred dynamical equation can also capture the spread of SARS and COVID-19 diseases. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.

References in corpus (27)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Catastrophic cascade of failures in interdependent networks
• Statistical physics of social dynamics
• Epidemic processes in complex networks
• Synchronization in complex networks
• Synchronization in small-world systems
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Detecting causal associations in large nonlinear time series datasets
• The physics of higher-order interactions in complex systems
• Structural Properties of the Caenorhabditis elegans Neuronal Network
• Discovering physical concepts with neural networks
• Predicting catastrophes in nonlinear dynamical systems by compressive sensing
• Small vulnerable sets determine large network cascades in power grids
• Analysis of a continuous-time model of structural balance
• Coupling functions: Universal insights into dynamical interaction mechanisms
• Model-free reconstruction of neuronal network connectivity from calcium imaging signals
• Network structure from rich but noisy data
• Causal Network Inference by Optimal Causation Entropy
• Inferring Network Topology from Complex Dynamics
• Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Stable Irregular Dynamics in Complex Neural Networks
• Inferring the dynamics of underdamped stochastic systems
• Inferring Global Dynamics Using a Learning Machine
• Inferring Global Dynamics of a Black-Box System Using Machine Learning
• Copy the dynamics using a learning machine

Cited by in corpus (10)

• Hypergraph reconstruction from dynamics
• Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data
• Backpropagation on Dynamical Networks
• Reconstructing dynamics of complex systems from noisy time series with hidden variables
• Observing network dynamics through sentinel nodes
• From reductionism to realism: Holistic mathematical modelling for complex biological systems
• Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes
• Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares
• Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel
• Spatio-Temporal Graphical Counterfactuals: An Overview