author = "Quiles, Marcos G. and Macau, Elbert Einstein Nehrer and Rubido, 
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidad de la 
                title = "Dynamical detection of network communities",
              journal = "Scientific Reports",
                 year = "2016",
               volume = "6",
                pages = "25570",
                month = "May",
             abstract = "structures. Specifically, communities are groups of nodes that are 
                         densely connected among each other but connect sparsely with 
                         others. However, detecting communities in networks is so far a 
                         major challenge, in particular, when networks evolve in time. 
                         Here, we propose a change in the community detection approach. It 
                         underlies in defining an intrinsic dynamic for the nodes of the 
                         network as interacting particles (based on diffusive equations of 
                         motion and on the topological properties of the network) that 
                         results in a fast convergence of the particle system into 
                         clustered patterns. The resulting patterns correspond to the 
                         communities of the network. Since our detection of communities is 
                         constructed from a dynamical process, it is able to analyse 
                         time-varying networks straightforwardly. Moreover, for static 
                         networks, our numerical experiments show that our approach 
                         achieves similar results as the methodologies currently recognized 
                         as the most efficient ones. Also, since our approach defines an 
                         N-body problem, it allows for efficient numerical implementations 
                         using parallel computations that increase its speed performance.",
                  doi = "10.1038/srep25570",
                  url = "http://dx.doi.org/10.1038/srep25570",
                 issn = "2045-2322",
             language = "en",
           targetfile = "quiles_dynamical.pdf",
        urlaccessdate = "24 jan. 2021"