Depicting network structures from measurable data is of significance. In real-world situations, it is common that some variables of networks are unavailable or even unknown. These unavailable and unknown variables, i.e., hidden variables, will lead to much reconstruction error, even make reconstruction methods useless. In this paper, to solve hidden variable problems, we propose three reconstruction methods, respectively, based on the following conditions: statistical characteristics of hidden variables, linearizable hidden variables, and white noise injection. Among them, the method based on white noise injection is active and invasive. In our framework, theoretic analyses of these three methods are given at first, and, furthermore, the validity of theoretical derivations and the robustness of these methods are fully verified through numerical results. Our work may be, therefore, helpful for practical experiments.
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January 2020
Research Article|
January 22 2020
Detecting network structures from measurable data produced by dynamics with hidden variables
Rundong Shi;
Rundong Shi
School of Sciences, Beijing University of Posts and Telecommunications
, Beijing 100876, China
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Weinuo Jiang
;
Weinuo Jiang
School of Sciences, Beijing University of Posts and Telecommunications
, Beijing 100876, China
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Shihong Wang
Shihong Wang
a)
School of Sciences, Beijing University of Posts and Telecommunications
, Beijing 100876, China
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a)
Author to whom correspondence should be addressed: shwang@bupt.edu.cn
Note: This paper is part of the Focus Issue, “When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics.”
Chaos 30, 013138 (2020)
Article history
Received:
September 08 2019
Accepted:
December 30 2019
Citation
Rundong Shi, Weinuo Jiang, Shihong Wang; Detecting network structures from measurable data produced by dynamics with hidden variables. Chaos 1 January 2020; 30 (1): 013138. https://doi.org/10.1063/1.5127052
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