Reconstructing network connections from measurable data facilitates our understanding of the mechanism of interactions between nodes. However, the unmeasurable nodes in real networks, also known as hidden nodes, introduce new challenges for reconstruction. There have been some hidden node detection methods, but most of them are limited by system models, network structures, and other conditions. In this paper, we propose a general theoretical method for detecting hidden nodes based on the random variable resetting method. We construct a new time series containing hidden node information based on the reconstruction results of random variable resetting, theoretically analyze the autocovariance of the time series, and finally provide a quantitative criterion for detecting hidden nodes. We numerically simulate our method in discrete and continuous systems and analyze the influence of main factors. The simulation results validate our theoretical derivation and illustrate the robustness of the detection method under different conditions.
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April 2023
Research Article|
April 03 2023
Detecting hidden nodes in networks based on random variable resetting method
Weinuo Jiang
;
Weinuo Jiang
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing)
School of Sciences, Beijing University of Posts and Telecommunications
, Beijing 100876, China
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Shihong Wang
Shihong Wang
a)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing)
School of Sciences, Beijing University of Posts and Telecommunications
, Beijing 100876, China
a)Author to whom correspondence should be addressed: shwang@bupt.edu.cn
Search for other works by this author on:
a)Author to whom correspondence should be addressed: shwang@bupt.edu.cn
Chaos 33, 043109 (2023)
Article history
Received:
November 15 2022
Accepted:
March 13 2023
Citation
Weinuo Jiang, Shihong Wang; Detecting hidden nodes in networks based on random variable resetting method. Chaos 1 April 2023; 33 (4): 043109. https://doi.org/10.1063/5.0134953
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