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Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model

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  • Liang Yang
  • Meng Ge
  • Di Jin
  • Dongxiao He
  • Huazhu Fu
  • Jing Wang
  • Xiaochun Cao

Abstract

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection.

Suggested Citation

  • Liang Yang & Meng Ge & Di Jin & Dongxiao He & Huazhu Fu & Jing Wang & Xiaochun Cao, 2017. "Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
  • Handle: RePEc:plo:pone00:0178029
    DOI: 10.1371/journal.pone.0178029
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    References listed on IDEAS

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    1. Ma, Xiaoke & Gao, Lin & Yong, Xuerong & Fu, Lidong, 2010. "Semi-supervised clustering algorithm for community structure detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 187-197.
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    2. Nan, Dong-Yang & Yu, Wei & Liu, Xiao & Zhang, Yun-Peng & Dai, Wei-Di, 2018. "A framework of community detection based on individual labels in attribute networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 523-536.

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