Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Comparison of graph clustering methods for analyzing the mathematical subject classification codes

  • Choi, Kwangju (Institute of Mathematical Sciences, Ewha Womans University) ;
  • Lee, June-Yub (Department of Mathematics, Ewha Womans University) ;
  • Kim, Younjin (Institute of Mathematical Sciences, Ewha Womans University) ;
  • Lee, Donghwan (Department of Statistics, Ewha Womans University)
  • Received : 2020.05.22
  • Accepted : 2020.07.08
  • Published : 2020.09.30
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Abstract

Various graph clustering methods have been introduced to identify communities in social or biological networks. This paper studies the entropy-based and the Markov chain-based methods in clustering the undirected graph. We examine the performance of two clustering methods with conventional methods based on quality measures of clustering. For the real applications, we collect the mathematical subject classification (MSC) codes of research papers from published mathematical databases and construct the weighted code-to-document matrix for applying graph clustering methods. We pursue to group MSC codes into the same cluster if the corresponding MSC codes appear in many papers simultaneously. We compare the MSC clustering results based on the several assessment measures and conclude that the Markov chain-based method is suitable for clustering the MSC codes.

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References

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