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Noise enhances information transfer in hierarchical networks

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enr...

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Detalles Bibliográficos
Autores principales: Czaplicka, Agnieszka, Holyst, Janusz A., Sloot, Peter M. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3565226/
https://www.ncbi.nlm.nih.gov/pubmed/23390574
http://dx.doi.org/10.1038/srep01223
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author Czaplicka, Agnieszka
Holyst, Janusz A.
Sloot, Peter M. A.
author_facet Czaplicka, Agnieszka
Holyst, Janusz A.
Sloot, Peter M. A.
author_sort Czaplicka, Agnieszka
collection PubMed
description We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.
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spelling pubmed-35652262013-02-06 Noise enhances information transfer in hierarchical networks Czaplicka, Agnieszka Holyst, Janusz A. Sloot, Peter M. A. Sci Rep Article We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. Nature Publishing Group 2013-02-06 /pmc/articles/PMC3565226/ /pubmed/23390574 http://dx.doi.org/10.1038/srep01223 Text en Copyright © 2013, 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
Czaplicka, Agnieszka
Holyst, Janusz A.
Sloot, Peter M. A.
Noise enhances information transfer in hierarchical networks
title Noise enhances information transfer in hierarchical networks
title_full Noise enhances information transfer in hierarchical networks
title_fullStr Noise enhances information transfer in hierarchical networks
title_full_unstemmed Noise enhances information transfer in hierarchical networks
title_short Noise enhances information transfer in hierarchical networks
title_sort noise enhances information transfer in hierarchical networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3565226/
https://www.ncbi.nlm.nih.gov/pubmed/23390574
http://dx.doi.org/10.1038/srep01223
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