@InProceedings{OliveiraQuilMaiaMaca:2015:CoDeLo,
               author = "Oliveira, Jo{\~a}o E. M. and Quiles, Marcos G. and Maia, Marcos 
                         Daniel Nogueira and Macau, Elbert Einstein Nehrer",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Community detection, with lower time complexity, using coupled 
                         Kuramoto oscillators",
            booktitle = "Proceedings...",
                 year = "2015",
                pages = "1160--1166",
         organization = "Annual ACM Symposium, 30.",
            publisher = "ACM Press",
             keywords = "Community Detection, Kuramoto Model, Time Complexity, Real 
                         Networks.",
             abstract = "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.",
  conference-location = "New York",
      conference-year = "13-17 Apr.",
                  doi = "10.1145/2695664.2695888",
                  url = "http://dx.doi.org/10.1145/2695664.2695888",
                 isbn = "9781450331968",
                label = "lattes: 5283661065432531 3 OliveiraQuilMaiaMaca:2015:CoDeLo",
             language = "en",
           targetfile = "1_oliveira.pdf",
                  url = "http://dl.acm.org/citation.cfm?id=2695888\&dl=ACM\&coll=DL\&CFID=712210634\&CFTOKEN=73142387",
        urlaccessdate = "27 abr. 2024"
}