Weighted multiplex networks.

One of the most important challenges in network science is to quantify the information encoded in complex network structures. Disentangling randomness from organizational principles is even more demanding when networks have a multiplex nature. Multiplex networks are multilayer systems of [Formula: s...

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Autores principales:	Giulia Menichetti, Daniel Remondini, Pietro Panzarasa, Raúl J Mondragón, Ginestra Bianconi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fc665f772d474cb1ba9949af92bc77e2
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spelling	oai:doaj.org-article:fc665f772d474cb1ba9949af92bc77e22021-11-18T08:16:50ZWeighted multiplex networks.1932-620310.1371/journal.pone.0097857https://doaj.org/article/fc665f772d474cb1ba9949af92bc77e22014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24906003/?tool=EBIhttps://doaj.org/toc/1932-6203One of the most important challenges in network science is to quantify the information encoded in complex network structures. Disentangling randomness from organizational principles is even more demanding when networks have a multiplex nature. Multiplex networks are multilayer systems of [Formula: see text] nodes that can be linked in multiple interacting and co-evolving layers. In these networks, relevant information might not be captured if the single layers were analyzed separately. Here we demonstrate that such partial analysis of layers fails to capture significant correlations between weights and topology of complex multiplex networks. To this end, we study two weighted multiplex co-authorship and citation networks involving the authors included in the American Physical Society. We show that in these networks weights are strongly correlated with multiplex structure, and provide empirical evidence in favor of the advantage of studying weighted measures of multiplex networks, such as multistrength and the inverse multiparticipation ratio. Finally, we introduce a theoretical framework based on the entropy of multiplex ensembles to quantify the information stored in multiplex networks that would remain undetected if the single layers were analyzed in isolation.Giulia MenichettiDaniel RemondiniPietro PanzarasaRaúl J MondragónGinestra BianconiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e97857 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giulia Menichetti Daniel Remondini Pietro Panzarasa Raúl J Mondragón Ginestra Bianconi Weighted multiplex networks.
description	One of the most important challenges in network science is to quantify the information encoded in complex network structures. Disentangling randomness from organizational principles is even more demanding when networks have a multiplex nature. Multiplex networks are multilayer systems of [Formula: see text] nodes that can be linked in multiple interacting and co-evolving layers. In these networks, relevant information might not be captured if the single layers were analyzed separately. Here we demonstrate that such partial analysis of layers fails to capture significant correlations between weights and topology of complex multiplex networks. To this end, we study two weighted multiplex co-authorship and citation networks involving the authors included in the American Physical Society. We show that in these networks weights are strongly correlated with multiplex structure, and provide empirical evidence in favor of the advantage of studying weighted measures of multiplex networks, such as multistrength and the inverse multiparticipation ratio. Finally, we introduce a theoretical framework based on the entropy of multiplex ensembles to quantify the information stored in multiplex networks that would remain undetected if the single layers were analyzed in isolation.
format	article
author	Giulia Menichetti Daniel Remondini Pietro Panzarasa Raúl J Mondragón Ginestra Bianconi
author_facet	Giulia Menichetti Daniel Remondini Pietro Panzarasa Raúl J Mondragón Ginestra Bianconi
author_sort	Giulia Menichetti
title	Weighted multiplex networks.
title_short	Weighted multiplex networks.
title_full	Weighted multiplex networks.
title_fullStr	Weighted multiplex networks.
title_full_unstemmed	Weighted multiplex networks.
title_sort	weighted multiplex networks.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/fc665f772d474cb1ba9949af92bc77e2
work_keys_str_mv	AT giuliamenichetti weightedmultiplexnetworks AT danielremondini weightedmultiplexnetworks AT pietropanzarasa weightedmultiplexnetworks AT rauljmondragon weightedmultiplexnetworks AT ginestrabianconi weightedmultiplexnetworks
_version_	1718422022607863808

Weighted multiplex networks.

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