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Fuzzy community structure detection by particle competition and cooperation

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Abstract

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

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Acknowledgments

This work was supported by the São Paulo State Research Foundation (FAPESP), the Brazilian National Research Council (CNPq), and the Foundation for the Development of Unesp (Fundunesp).

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  1. Fabricio Breve

    Present address: Department of Statistics, Applied Mathematics and Computation (DEMAC), Institute of Geosciences and Exact Sciences (IGCE), São Paulo State University (UNESP), Avenida 24 A, 1515, 13506-900, Rio Claro, SP, Brazil

Authors and Affiliations

  1. Department of Computer Science, Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), Av. Trabalhador São-carlense, 400, 13560-970, São Carlos, SP, Brazil

    Fabricio Breve & Liang Zhao

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  1. Fabricio Breve
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  2. Liang Zhao
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Correspondence to Fabricio Breve.

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Breve, F., Zhao, L. Fuzzy community structure detection by particle competition and cooperation. Soft Comput 17, 659–673 (2013). https://doi.org/10.1007/s00500-012-0924-3

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