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The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network

The stochastic dynamics of conserved quantities is an emergent phenomena observed in many complex systems, ranging from social and to biological networks. Using an extension of the Ehrenfest urn model on a complex network, over which a conserved quantity is transported in a random fashion, we study...

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Autores principales: Medina, Pablo, Clark, Jaime, Kiwi, Miguel, Torres, Felipe, Rogan, José, Valdivia, Juan Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155166/
https://www.ncbi.nlm.nih.gov/pubmed/30250266
http://dx.doi.org/10.1038/s41598-018-32677-8
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author Medina, Pablo
Clark, Jaime
Kiwi, Miguel
Torres, Felipe
Rogan, José
Valdivia, Juan Alejandro
author_facet Medina, Pablo
Clark, Jaime
Kiwi, Miguel
Torres, Felipe
Rogan, José
Valdivia, Juan Alejandro
author_sort Medina, Pablo
collection PubMed
description The stochastic dynamics of conserved quantities is an emergent phenomena observed in many complex systems, ranging from social and to biological networks. Using an extension of the Ehrenfest urn model on a complex network, over which a conserved quantity is transported in a random fashion, we study the dynamics of many elementary packets transported through the network by means of a master equation approach and compare with the mean field approximation and stochastic simulations. By use of the mean field theory, it is possible to compute an approximation to the ensemble average evolution of the number of packets in each node which, in the thermodynamic limit, agrees quite well with the results of the master equation. However, the master equation gives a more complete description of the stochastic system and provides a probabilistic view of the occupation number at each node. Of particular relevance is the standard deviation of the occupation number at each node, which is not uniform for a complex network. We analyze and compare different network topologies (small world, scale free, Erdos-Renyi, among others). Given the computational complexity of directly evaluating the asymptotic, or equilibrium, occupation number probability distribution, we propose a scaling relation with the number of packets in the network, that allows to construct the asymptotic probability distributions from the network with one packet. The approximation, which relies on the same matrix found in the mean field approach, becomes increasingly more accurate for a large number of packets.
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spelling pubmed-61551662018-09-28 The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network Medina, Pablo Clark, Jaime Kiwi, Miguel Torres, Felipe Rogan, José Valdivia, Juan Alejandro Sci Rep Article The stochastic dynamics of conserved quantities is an emergent phenomena observed in many complex systems, ranging from social and to biological networks. Using an extension of the Ehrenfest urn model on a complex network, over which a conserved quantity is transported in a random fashion, we study the dynamics of many elementary packets transported through the network by means of a master equation approach and compare with the mean field approximation and stochastic simulations. By use of the mean field theory, it is possible to compute an approximation to the ensemble average evolution of the number of packets in each node which, in the thermodynamic limit, agrees quite well with the results of the master equation. However, the master equation gives a more complete description of the stochastic system and provides a probabilistic view of the occupation number at each node. Of particular relevance is the standard deviation of the occupation number at each node, which is not uniform for a complex network. We analyze and compare different network topologies (small world, scale free, Erdos-Renyi, among others). Given the computational complexity of directly evaluating the asymptotic, or equilibrium, occupation number probability distribution, we propose a scaling relation with the number of packets in the network, that allows to construct the asymptotic probability distributions from the network with one packet. The approximation, which relies on the same matrix found in the mean field approach, becomes increasingly more accurate for a large number of packets. Nature Publishing Group UK 2018-09-24 /pmc/articles/PMC6155166/ /pubmed/30250266 http://dx.doi.org/10.1038/s41598-018-32677-8 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Medina, Pablo
Clark, Jaime
Kiwi, Miguel
Torres, Felipe
Rogan, José
Valdivia, Juan Alejandro
The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title_full The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title_fullStr The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title_full_unstemmed The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title_short The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network
title_sort stochastic transport dynamics of a conserved quantity on a complex network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155166/
https://www.ncbi.nlm.nih.gov/pubmed/30250266
http://dx.doi.org/10.1038/s41598-018-32677-8
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