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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Dynamical detection of network communities

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Author(s):
Quiles, Marcos G. [1] ; Macau, Elbert E. N. [2] ; Rubido, Nicolas [3, 4]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Paulo Unifesp, Dept Sci & Technol DCT, BR-12247014 Sao Jose Dos Campos, SP - Brazil
[2] Inst Nacl Pesquisas Espaciais, Lab Associado Computacao & Matemat Aplicada, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[3] Univ Republica, Fac Ciencias, Inst Fis, Igua 4225, Montevideo 11400 - Uruguay
[4] Univ Aberdeen, Kings Coll, Inst Complex Syst & Math Biol, SUPA, Aberdeen AB24 3UE - Scotland
Total Affiliations: 4
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 6, MAY 9 2016.
Web of Science Citations: 6
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. (AU)

FAPESP's process: 11/50151-0 - Dynamical phenomena in complex networks: fundamentals and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants
FAPESP's process: 11/18496-7 - Dynamic semi-supervised and active learning based on complex networks
Grantee:Marcos Gonçalves Quiles
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants