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Uncovering hidden nodes in complex networks in the presence of noise

Ascertaining the existence of hidden objects in a complex system, objects that cannot be observed from the external world, not only is curiosity-driven but also has significant practical applications. Generally, uncovering a hidden node in a complex network requires successful identification of its...

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Detalles Bibliográficos
Autores principales: Su, Ri-Qi, Lai, Ying-Cheng, Wang, Xiao, Do, Younghae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3909906/
https://www.ncbi.nlm.nih.gov/pubmed/24487720
http://dx.doi.org/10.1038/srep03944
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author Su, Ri-Qi
Lai, Ying-Cheng
Wang, Xiao
Do, Younghae
author_facet Su, Ri-Qi
Lai, Ying-Cheng
Wang, Xiao
Do, Younghae
author_sort Su, Ri-Qi
collection PubMed
description Ascertaining the existence of hidden objects in a complex system, objects that cannot be observed from the external world, not only is curiosity-driven but also has significant practical applications. Generally, uncovering a hidden node in a complex network requires successful identification of its neighboring nodes, but a challenge is to differentiate its effects from those of noise. We develop a completely data-driven, compressive-sensing based method to address this issue by utilizing complex weighted networks with continuous-time oscillatory or discrete-time evolutionary-game dynamics. For any node, compressive sensing enables accurate reconstruction of the dynamical equations and coupling functions, provided that time series from this node and all its neighbors are available. For a neighboring node of the hidden node, this condition cannot be met, resulting in abnormally large prediction errors that, counterintuitively, can be used to infer the existence of the hidden node. Based on the principle of differential signal, we demonstrate that, when strong noise is present, insofar as at least two neighboring nodes of the hidden node are subject to weak background noise only, unequivocal identification of the hidden node can be achieved.
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spelling pubmed-39099062014-02-03 Uncovering hidden nodes in complex networks in the presence of noise Su, Ri-Qi Lai, Ying-Cheng Wang, Xiao Do, Younghae Sci Rep Article Ascertaining the existence of hidden objects in a complex system, objects that cannot be observed from the external world, not only is curiosity-driven but also has significant practical applications. Generally, uncovering a hidden node in a complex network requires successful identification of its neighboring nodes, but a challenge is to differentiate its effects from those of noise. We develop a completely data-driven, compressive-sensing based method to address this issue by utilizing complex weighted networks with continuous-time oscillatory or discrete-time evolutionary-game dynamics. For any node, compressive sensing enables accurate reconstruction of the dynamical equations and coupling functions, provided that time series from this node and all its neighbors are available. For a neighboring node of the hidden node, this condition cannot be met, resulting in abnormally large prediction errors that, counterintuitively, can be used to infer the existence of the hidden node. Based on the principle of differential signal, we demonstrate that, when strong noise is present, insofar as at least two neighboring nodes of the hidden node are subject to weak background noise only, unequivocal identification of the hidden node can be achieved. Nature Publishing Group 2014-02-03 /pmc/articles/PMC3909906/ /pubmed/24487720 http://dx.doi.org/10.1038/srep03944 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Article
Su, Ri-Qi
Lai, Ying-Cheng
Wang, Xiao
Do, Younghae
Uncovering hidden nodes in complex networks in the presence of noise
title Uncovering hidden nodes in complex networks in the presence of noise
title_full Uncovering hidden nodes in complex networks in the presence of noise
title_fullStr Uncovering hidden nodes in complex networks in the presence of noise
title_full_unstemmed Uncovering hidden nodes in complex networks in the presence of noise
title_short Uncovering hidden nodes in complex networks in the presence of noise
title_sort uncovering hidden nodes in complex networks in the presence of noise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3909906/
https://www.ncbi.nlm.nih.gov/pubmed/24487720
http://dx.doi.org/10.1038/srep03944
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