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Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain


Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

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Advantages of Edge-centric Collective Dynamics in Machine Learning Tasks

Journal of Applied Nonlinear Dynamics 7(3) (2018) 269--285 | DOI:10.5890/JAND.2018.09.005

Filipe Alves Neto Verri$^{1}$,$^{2}$, Paulo Roberto Urio$^{3}$, Liang Zhao$^{4}$

$^{1}$ Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil

$^{2}$ School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe AZ, USA

$^{3}$ Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil

$^{4}$ Ribeir˜ao Preto School of Philosophy, Science and Literature, University of São Paulo, Ribeirão Preto, Brazil

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We study how effectively edge-centric dynamics solve semi-supervised learning tasks. The Edge Domination System is an algorithm to reveal patterns and obtain information of the underlying complex network. The algorithm consists of the simulation of a collective dynamical system based on particle competition for the dominance of edges. In this paper, we propose a vertex-centric version of this model and assess the differences between the edge-centric model. The edge-centric system offers better features in semi-supervised learning tasks, such as greater exploration behavior and faster convergence.


This research was supported by the S˜ao Paulo State Research Foundation (FAPESP), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the Brazilian National Research Council (CNPq).


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