Particle Competition in Complex Networks for Semi-supervised Classification

Fabricio Breve; Liang Zhao; Marcos Quiles

Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1

Research Article

Particle Competition in Complex Networks for Semi-supervised Classification

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@INPROCEEDINGS{10.1007/978-3-642-02466-5_14,
    author={Fabricio Breve and Liang Zhao and Marcos Quiles},
    title={Particle Competition in Complex Networks for Semi-supervised Classification},
    proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1},
    proceedings_a={COMPLEX PART 1},
    year={2012},
    month={5},
    keywords={semi-supervised learning particle competition complex networks community detection},
    doi={10.1007/978-3-642-02466-5_14}
}

Fabricio Breve
Liang Zhao
Marcos Quiles
Year: 2012
Particle Competition in Complex Networks for Semi-supervised Classification
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_14

Fabricio Breve¹^,*, Liang Zhao¹^,*, Marcos Quiles¹^,*

1: University of São Paulo

*Contact email: fabricio@icmc.usp.br, zhao@icmc.usp.br, quiles@icmc.usp.br

Abstract

Semi-supervised learning is an important topic in machine learning. In this paper, a network-based semi-supervised classification method is proposed. Class labels are propagated by combined random-deterministic walking of particles and competition among them. Different from other graph-based methods, our model does not rely on loss function or regularizer. Computer simulations were performed with synthetic and real data, which show that the proposed method can classify arbitrarily distributed data, including linear non-separable data. Moreover, it is much faster due to lower order of complexity and it can achieve better results with few pre-labeled data than other graph based methods.

Keywords: semi-supervised learning particle competition complex networks community detection

Published: 2012-05-11

: http://dx.doi.org/10.1007/978-3-642-02466-5_14