Advantages of Edge-centric Collective Dynamics in Machine Learning Tasks

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Authors

  • Filipe Alves Neto Verri Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe AZ, USA Author
  • Paulo Roberto Urio Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil Author
  • Liang Zhao Ribeir˜ao Preto School of Philosophy, Science and Literature, University of São Paulo, Ribeirão Preto, Brazil Author

DOI:

https://doi.org/10.5890/JAND.2018.09.005

Abstract

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.

References

[1] Gueleri, R.A., Cupertino, T.H., de Carvalho, A.C.P.L.F., and Zhao, L. (2014), A ocking-like technique to perform semi-supervised learning, International Joint Conference on Neural Networks (IJCNN), IEEE, 1579- 1586, DOI: 10.1109/IJCNN.2014.6889434.

[2] Zhang, Z., Long, K.,Wang, J., and Dressler, F. (2014), On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches, IEEE Commun. Surveys Tuts., 16(1), 513-537.

[3] Breve, F., Quiles, M.G., and Zhao, L. (2015), Interactive image segmentation using particle competition and cooperation, International Joint Conference in Neural Networks, Proceedings, IEEE, 1-8.

[4] Verri, F.A.N., Urio, P.R., and Zhao, L. (2016), Network Unfolding Map by Vertex-Edge Dynamics Modeling, IEEE Transactions on Neural Networks and Learning Systems, (99), 1-14, ISSN: 2162-237X, DOI: 10.1109/TNNLS.2016.2626341.

[5] Silva, T.C. and Zhao, L. (2016), Machine Learning in Complex Networks, 1st ed., Springer, p. 331, ISBN: 978-3-319-17290-3, DOI: 10.1007/978-3-319-17290-3.

[6] Urio, P.R., Verri, F.A.N., and Zhao, L. (2016), Features of edge-centric collective dynamics in machine learning tasks, International Conference on Nonlinear Science and Complexity, Proceedings.

[7] Chapelle, O., Schölkopf, B., and Zien, A., eds. (2006), Semi-Supervised Learning, MIT Press: Cambridge, MA, p. 524, ISBN: 9780262033589, DOI: 10.7551/mitpress/9780262033589.001.0001.

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PublishedSeptember 2018

Usage tracking begins September 1, 2026.

History Published

Issue

Vol. 7 No. 3 (2018): Regular Issue

Section

Research Articles

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Unless an article landing page states a Creative Commons or other article-specific license, the article is published by L&H Scientific Publishing, LLC and all rights are reserved. The article-level license and copyright notice govern access and reuse.

How to Cite

Verri, F. A. N., Urio, P. R., & Zhao, L. (2026). Advantages of Edge-centric Collective Dynamics in Machine Learning Tasks. Journal of Applied Nonlinear Dynamics, 7(3), 269-285. https://doi.org/10.5890/JAND.2018.09.005

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