Rough Net Approach for Community Detection Analysis in Complex Networks

Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection valid...

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Autores principales: Fuentes, Ivett, Pina, Arian, Nápoles, Gonzalo, Arco, Leticia, Vanhoof, Koen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338191/
http://dx.doi.org/10.1007/978-3-030-52705-1_30
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author Fuentes, Ivett
Pina, Arian
Nápoles, Gonzalo
Arco, Leticia
Vanhoof, Koen
author_facet Fuentes, Ivett
Pina, Arian
Nápoles, Gonzalo
Arco, Leticia
Vanhoof, Koen
author_sort Fuentes, Ivett
collection PubMed
description Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.
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spelling pubmed-73381912020-07-07 Rough Net Approach for Community Detection Analysis in Complex Networks Fuentes, Ivett Pina, Arian Nápoles, Gonzalo Arco, Leticia Vanhoof, Koen Rough Sets Article Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new community interaction visualization approach combining both complex network representation and Rough Net definition is adopted to interpret the community structure. We provide some examples that illustrate how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks. 2020-06-10 /pmc/articles/PMC7338191/ http://dx.doi.org/10.1007/978-3-030-52705-1_30 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Fuentes, Ivett
Pina, Arian
Nápoles, Gonzalo
Arco, Leticia
Vanhoof, Koen
Rough Net Approach for Community Detection Analysis in Complex Networks
title Rough Net Approach for Community Detection Analysis in Complex Networks
title_full Rough Net Approach for Community Detection Analysis in Complex Networks
title_fullStr Rough Net Approach for Community Detection Analysis in Complex Networks
title_full_unstemmed Rough Net Approach for Community Detection Analysis in Complex Networks
title_short Rough Net Approach for Community Detection Analysis in Complex Networks
title_sort rough net approach for community detection analysis in complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338191/
http://dx.doi.org/10.1007/978-3-030-52705-1_30
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