English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/131721
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions

AuthorsGómez, Daniel; Tinguaro Rodríguez, J.; Yáñez, Javier; Montero, Javier
KeywordsAggregation operators
Overlap functions
Grouping functions
Issue Date30-Mar-2016
PublisherElsevier
CitationInternational Journal of Approximate Reasoning, 74: 88–107 (2016)
AbstractOne of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions.
Publisher version (URL)http://dx.doi.org/10.1016/j.ijar.2016.03.003
URIhttp://hdl.handle.net/10261/131721
DOI10.1016/j.ijar.2016.03.003
ISSN0888-613X)
Appears in Collections:(IGEO) Artículos
Files in This Item:
File Description SizeFormat 
IJAR_2016_74_88.pdf416,71 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.