@Article{QuilesMacaRubi:2016:DyDeNe,
author = "Quiles, Marcos G. and Macau, Elbert Einstein Nehrer and Rubido,
Nicol{\'a}s",
affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidad de la
Rep{\'u}blica}",
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 = "07 maio 2024"
}