Particle competition and cooperation for uncovering network overlap community structure

  • Authors:

  • Fabricio Breve;Liang Zhao;Marcos Quiles;Witold Pedrycz;Jiming Liu

  • Affiliations:

  • Department of Computation, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil;Department of Computation, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil;Department of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Computer Science Department, Hong Kong Baptist University, Kowloon, Hong Kong

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
  • Year:
  • 2011

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Abstract

Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graphbased (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.