Detecting network structures from measurable data produced by dynamics with hidden variables

DOI10.1063/1.5127052zbMath1437.62700OpenAlexW3001184417WikidataQ89512525 ScholiaQ89512525MaRDI QIDQ5218170

Weinuo Jiang, Rundong Shi, Shihong Wang

Publication date: 28 February 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/1.5127052

zbMATH Keywords

white noisehidden variablesnetwork structures

Mathematics Subject Classification ID

White noise theory (60H40) Neural nets and related approaches to inference from stochastic processes (62M45) Statistical aspects of big data and data science (62R07)

Related Items (1)

A real-time identification method of network structure in complex network systems

Cites Work

• Reconstructing networks from dynamics with correlated noise
• Reconstructing nonlinear networks subject to fast-varying noises by using linearization with expanded variables
• Reverse engineering of complex dynamical networks in the presence of time-delayed interactions based on noisy time series
• Revealing networks from dynamics: an introduction
• Inverting chaos: Extracting system parameters from experimental data
• Inferring network topology from complex dynamics

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