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		<doi>10.1016/j.cnsns.2017.05.002</doi>
		<issn>1007-5704</issn>
		<citationkey>MaiaOlivQuilMaca:2017:CoDeCo</citationkey>
		<title>Community detection in complex networks via adapted Kuramoto dynamics</title>
		<year>2017</year>
		<month>Dec.</month>
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		<author>Maia, Daniel Marcos Nogueira,</author>
		<author>Oliveira, João Eliakin Mota de,</author>
		<author>Quiles, Marcos G.,</author>
		<author>Macau, Elbert Einstein Nehrer,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>maia@physik.hu-berlin.de</electronicmailaddress>
		<electronicmailaddress>joaoeliakin@gmail.com</electronicmailaddress>
		<electronicmailaddress>quiles@unifesp.br</electronicmailaddress>
		<electronicmailaddress>elbert.macau@inpe.br</electronicmailaddress>
		<journal>Communications in Nonlinear Science and Numerical Simulation</journal>
		<volume>53</volume>
		<pages>130-141</pages>
		<secondarymark>A1_INTERDISCIPLINAR A1_ENGENHARIAS_III A2_ENGENHARIAS_IV A2_ENGENHARIAS_I A2_CIÊNCIA_DE_ALIMENTOS B1_ASTRONOMIA_/_FÍSICA B2_CIÊNCIA_DA_COMPUTAÇÃO B3_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA</secondarymark>
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		<keywords>Community detection, Kuramoto model.</keywords>
		<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.</abstract>
		<area>COMP</area>
		<language>en</language>
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