%0 Journal Article
%4 sid.inpe.br/mtc-m21b/2017/05.31.18.21
%2 sid.inpe.br/mtc-m21b/2017/05.31.18.21.25
%@doi 10.1016/j.cnsns.2017.05.002
%@issn 1007-5704
%T Community detection in complex networks via adapted Kuramoto dynamics
%D 2017
%8 Dec.
%9 journal article
%A Maia, Daniel Marcos Nogueira,
%A Oliveira, João Eliakin Mota de,
%A Quiles, Marcos G.,
%A Macau, Elbert Einstein Nehrer,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress maia@physik.hu-berlin.de
%@electronicmailaddress joaoeliakin@gmail.com
%@electronicmailaddress quiles@unifesp.br
%@electronicmailaddress elbert.macau@inpe.br
%B Communications in Nonlinear Science and Numerical Simulation
%V 53
%P 130-141
%K Community detection, Kuramoto model.
%X 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.
%@language en
%3 maia_community.pdf