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A random interacting network model for complex networks

We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link betw...

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Autores principales: Goswami, Bedartha, Shekatkar, Snehal M., Rheinwalt, Aljoscha, Ambika, G., Kurths, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4676066/
https://www.ncbi.nlm.nih.gov/pubmed/26657032
http://dx.doi.org/10.1038/srep18183
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author Goswami, Bedartha
Shekatkar, Snehal M.
Rheinwalt, Aljoscha
Ambika, G.
Kurths, Jürgen
author_facet Goswami, Bedartha
Shekatkar, Snehal M.
Rheinwalt, Aljoscha
Ambika, G.
Kurths, Jürgen
author_sort Goswami, Bedartha
collection PubMed
description We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.
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spelling pubmed-46760662015-12-16 A random interacting network model for complex networks Goswami, Bedartha Shekatkar, Snehal M. Rheinwalt, Aljoscha Ambika, G. Kurths, Jürgen Sci Rep Article We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. Nature Publishing Group 2015-12-11 /pmc/articles/PMC4676066/ /pubmed/26657032 http://dx.doi.org/10.1038/srep18183 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Goswami, Bedartha
Shekatkar, Snehal M.
Rheinwalt, Aljoscha
Ambika, G.
Kurths, Jürgen
A random interacting network model for complex networks
title A random interacting network model for complex networks
title_full A random interacting network model for complex networks
title_fullStr A random interacting network model for complex networks
title_full_unstemmed A random interacting network model for complex networks
title_short A random interacting network model for complex networks
title_sort random interacting network model for complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4676066/
https://www.ncbi.nlm.nih.gov/pubmed/26657032
http://dx.doi.org/10.1038/srep18183
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