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2013 13th International Conference on Computational Science and Its Applications

2012 12th International Conference on Computational Science and Its Applications

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2008 International Conference on Computational Sciences and Its Applications

2007 International Conference on Computational Science and its Applications (ICCSA 2007)

Community Detection in Complex Networks Using Coupled Kuramoto Oscillators

Content Provider	IEEE Xplore Digital Library
Author	Mota de Oliveira, J.E. Quiles, M.G.
Copyright Year	2014
Description	Author affiliation: Inst. of Sci. & Technol., Fed. Univ. of Sao Paulo, Sao Joso dos Campos, Brazil (Mota de Oliveira, J.E.; Quiles, M.G.)
Abstract	Recently, complex networks have been used to represent natural and artificial agents and their relationships. A common feature in these networks is the presence of communities or modular structures in which a vertex related to a determined community is, proportionally, more densely connected to other vertices belonging to its own community than to the rest of the network. Several approaches have been proposed dictating a dynamic rule for the vertices based on the topology of the network, in other words, the dense connectivity of the vertices inside a community would provide similar values for the metric used in the dynamics, which could be used as a way to determine the eventual communities existing in the network. In this paper, the rule for the dynamics is the Kuramoto's synchronization model for coupled oscillators. In this scenario, the network is interpreted as composed of oscillators obeying this synchronization model. Since in its original form this model does not realize communities, a modified one is used, where phases within vertices of a same community evolve together to a final common phase value and vertices of different communities are forced to have their phases far different when the dynamic equilibrium is reached. To verify the correctness of this approach, the model has been tested on Girvan-Newman's benchmark networks, ranging the mixing parameter from a scenario in which the communities are completely isolated to one in which the community is structure is barely observed. These tests have provided good results on detecting the existing communities, even in the most difficult cases. Minor tests were also made on symmetrical and assymetrical Lancichinetti-Fortunato-Radicchi networks (LFR).
Starting Page	85
Ending Page	90
File Size	983799
Page Count	6
File Format	PDF
ISBN	9781479942640
DOI	10.1109/ICCSA.2014.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-30
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Communities Oscillators Complex networks Synchronization Couplings Distance measurement Joining processes Community detection Kuramoto oscillator
Content Type	Text
Resource Type	Article

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