Detecting network structures from measurable data produced by dynamics with hidden variables-Reference-Cited by-同舟云学术

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

Published:2020-01 Issue:1 Volume:30 Page:013138
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Shi Rundong¹,Jiang Weinuo¹^ORCID,Wang Shihong¹

Affiliation:

1. School of Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5127052

Reference37 articles.

1. Complex brain networks: graph theoretical analysis of structural and functional systems

2. Weak pairwise correlations imply strongly correlated network states in a neural population

3. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks

4. Genome Sequence of the Nematode C. elegans : A Platform for Investigating Biology

5. Network biology: understanding the cell's functional organization

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1. Reconstructing dynamics of complex systems from noisy time series with hidden variables;New Journal of Physics;2023-08-01

2. Detecting hidden nodes in networks based on random variable resetting method;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-04-01

3. Network Structure Identification Based on Measured Output Data Using Koopman Operators;Mathematics;2022-12-26

4. Reconstruction of complex network from time series data based on graph attention network and Gumbel Softmax;International Journal of Modern Physics C;2022-10-27

5. A real-time identification method of network structure in complex network systems;International Journal of Systems Science;2022-10-20

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