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Automatic Classification of Department Types and Analysis of Co-Authorship Network: Focusing on Korean Journals in the Computer Field

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(4), pp.53-63
  • DOI : 10.9708/jksci.2023.28.04.053
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 14, 2023
  • Accepted : April 12, 2023
  • Published : April 28, 2023

Byungkyu Kim 1 Beom-Jong You 1 Min-Woo Park 1

1한국과학기술정보연구원

Accredited

ABSTRACT

The utilization of department information in bibliometric analysis using scientific and technological literature is highly advantageous. In this paper, the department information dataset was built through the screening, data refinement, and classification processing of authors’ department type belonging to university institutions appearing in academic journals in the field of science and technology published in Korea, and the automatic classification model based on deep learning was developed using the department information dataset as learning data and verification data. In addition, we analyzed the co-authorship structure and network in the field of computer science using the department information dataset and affiliation information of authors from domestic academic journals. The research resulted in a 98.6% accuracy rate for the automatic classification model using Korean department information. Moreover, the co-authorship patterns of Korean researchers in the computer science and engineering field, along with the characteristics and centralities of the co-author network based on institution type, region, institution, and department type, were identified in detail and visually presented on a map.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.