Community detection in complex networks via adapted Kuramoto dynamics

Community detection in complex networks via adapted Kuramoto dynamics

Author Maia, Daniel M. N. Google Scholar
de Oliveira, Joao E. M. Google Scholar
Quiles, Marcos G. Autor UNIFESP Google Scholar
Macau, Elbert E. N. Google Scholar
Abstract Based on the Kuramoto model, a new network model, namely, the generalized Kuramoto model with Fourier term, is introduced for studying community detection in complex networks. In particular, the Fourier term provides a natural phase locking of the trajectories into a pre-defined number of clusters. A mathematical approach is used to study the behavior of the solutions and its properties. Conditions for properly choosing the coupling parameters so that phase locking takes place are presented and a quality function called clustering density is introduced to measure the effectiveness of the communities identification. Illustrations with real and synthetic networks with community structure are presented. (C) 2017 Elsevier B.V. All rights reserved.
Keywords Community detection
Kuramoto model
xmlui.dri2xhtml.METS-1.0.item-coverage Amsterdam
Language English
Sponsor CAPES
Grant number CNPq: 311467/2014-8
CNPq: 458070/2014-9
FAPESP: 2015/50122-0
Date 2017
Published in Communications In Nonlinear Science And Numerical Simulation. Amsterdam, v. 53, p. 130-141, 2017.
ISSN 1007-5704 (Sherpa/Romeo, impact factor)
Publisher Elsevier Science Bv
Extent 130-141
Access rights Closed access
Type Article
Web of Science ID WOS:000404307400010

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