%0 Conference Proceedings
%4 sid.inpe.br/plutao/2015/12.04.11.28.04
%2 sid.inpe.br/plutao/2015/12.04.11.28.05
%@doi 10.1145/2695664.2695888
%@isbn 9781450331968
%F lattes: 5283661065432531 3 OliveiraQuilMaiaMaca:2015:CoDeLo
%T Community detection, with lower time complexity, using coupled Kuramoto oscillators
%D 2015
%A Oliveira, João E. M.,
%A Quiles, Marcos G.,
%A Maia, Marcos Daniel Nogueira,
%A Macau, Elbert Einstein Nehrer,
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress joao.eliakin@unifesp.br
%@electronicmailaddress quiles@unifesp.br
%@electronicmailaddress mdanielnm@gmail.com
%@electronicmailaddress elbert.macau@inpe.br
%B Annual ACM Symposium, 30
%C New York
%8 13-17 Apr.
%I ACM Press
%P 1160-1166
%S Proceedings
%K Community Detection, Kuramoto Model, Time Complexity, Real Networks.
%X For about two decades, the research topic of Complex Networks has been presented ubiquitously. As a simple and effective framework to express agents and their relationships, several fields of study, from Physics to Sociology, have taken advantage of the powerful representation provided by complex networks. A particular feature inherited by almost any real world network is the presence of densely connected groups of vertices, named modules, clusters or communities. The majority of the proposed techniques does not take advantage of specific features commonly encountered on real networks, such as the power law distribution of vertices degree (presence of hubs) and its dynamic nature, i.e. vertices, edges and communities normally does not persist invariant regarding to time. Aiming to take into account these two important features, an another ubiquitous phenomenon is applied on detecting communities: synchronization, expressed by coupled Kuramoto oscillators. Here, we extend the Kuramotos model by introducing a negative coupling between hubs in the network. Moreover, two adjacency lists are used to represent, efficiently, the network structure. Tests have been performed in real network benchmarks, with consistent results achieved.
%@language en
%3 1_oliveira.pdf
%U http://dl.acm.org/citation.cfm?id=2695888&dl=ACM&coll=DL&CFID=712210634&CFTOKEN=73142387