Quantifying layer similarity in multiplex networks: A systematic study

Bródka, P; Chmiel, A; Magnani, M; Ragozini, G

Quantifying layer similarity in multiplex networks: A systematic study

Bródka, P

Chmiel, A Magnani, M Ragozini, G

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Publication Type:: Journal Article
Citation:: Royal Society Open Science, 2018, 5 (8)
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Bródka, P https://orcid.org/0000-0002-6474-0089	en_US
dc.contributor.author	Chmiel, A	en_US
dc.contributor.author	Magnani, M	en_US
dc.contributor.author	Ragozini, G	en_US
dc.date.available	2018-07-04	en_US
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	Royal Society Open Science, 2018, 5 (8)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132529
dc.description.abstract	© 2018 The Authors. Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.	en_US
dc.relation.ispartof	Royal Society Open Science	en_US
dc.relation.isbasedon	10.1098/rsos.171747	en_US
dc.title	Quantifying layer similarity in multiplex networks: A systematic study	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	5	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2018 The Authors. Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

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http://hdl.handle.net/10453/132529