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Soft Computing

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01-04-2013 | Original Paper

Fuzzy community structure detection by particle competition and cooperation

Authors: Fabricio Breve, Liang Zhao

Published in: Soft Computing | Issue 4/2013

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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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Title: Fuzzy community structure detection by particle competition and cooperation
Authors: Fabricio Breve
Liang Zhao
Publication date: 01-04-2013
Publisher: Springer-Verlag
Published in: Soft Computing / Issue 4/2013
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0924-3

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