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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/74902
Title: 
Fuzzy community structure detection by particle competition and cooperation
Author(s): 
Institution: 
  • Universidade de São Paulo (USP)
  • Universidade Estadual Paulista (UNESP)
ISSN: 
  • 1432-7643
  • 1433-7479
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. © 2012 Springer-Verlag.
Issue Date: 
1-Apr-2013
Citation: 
Soft Computing, v. 17, n. 4, p. 659-673, 2013.
Time Duration: 
659-673
Keywords: 
  • Community detection
  • Graph-based method
  • Outliers
  • Overlapping nodes
  • Particle competition and cooperation
  • Graph-based methods
  • Particle competition and cooperations
  • Graphic methods
  • Supervised learning
  • Virtual reality
  • Statistics
Source: 
http://dx.doi.org/10.1007/s00500-012-0924-3
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/74902
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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