Comparison of graph clustering methods for analyzing the mathematical subject classification codes

• Published in: Communications for statistical applications and methods Vol. 27; no. 5; pp. 569 - 578
• Main Authors: Choi, Kwangju, Lee, June-Yub, Kim, Younjin, Lee, Donghwan
• Format: Journal Article
• Language: Korean
• Published: 한국통계학회 30.09.2020
• Subjects: entropy graph clustering; Markov chain clustering; mathematical subject classification; entropy graph clustering; mathematical subject classification; Markov chain clustering
• ISSN: 2287-7843

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.