Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Comparison of journal clustering methods based on citation structure

논문 인용에 따른 학술지 군집화 방법의 비교

  • Kim, Jinkwang (Department of Statistics, Yeungnam University) ;
  • Kim, Sohyung (Academic Infrastructure Promotion Team, National Research Foundation of Korea) ;
  • Oh, Changhyuck (Department of Statistics, Yeungnam University)
  • 김진광 (영남대학교 통계학과) ;
  • 김소형 (한국연구재단 학술기반진흥팀) ;
  • 오창혁 (영남대학교 통계학과)
  • Received : 2015.04.03
  • Accepted : 2015.06.20
  • Published : 2015.07.31
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Abstract

Extraction of communities from a journal citation database by the citation structure is a useful tool to see closely related groups of the journals. SCI of Thomson Reuters or SCOPUS of Elsevier have had tried to grasp community structure of the journals in their indices according to citation relationships, but such a trial has not been made yet with the Korean Citation Index, KCI. Therefore, in this study, we extracted communities of the journals of the natural science area in KCI, using various clustering algorithms for a social network based on citations among the journals and compared the groups obtained with the classfication of KCI. The infomap algorithm, one of the clustering methods applied in this article, showed the best grouping result in the sense that groups obtained by it are closer to the KCI classification than by other algorithms considered and reflect well the citation structure of the journals. The classification results obtained in this study might be taken consideration when reclassification of the KCI journals will be made in the future.

학술지 인용 데이터베이스에서 네트워크 구조분석을 통해 학술지의 공동체를 추출하는 것은 인용관계에 따른 학술지의 집단을 파악하는 유용한 수단이다. 전 세계적으로 널리 활용되는 학술지 인용데이터베이스인 Thomson Reuters의 SCI나 Elsevier의 SCOPUS가 제공하는 자료를 활용하여 인용관계에 따른 공동체 구조를 파악하는 시도가 이루어진 바 있으나, 국내 학술지 인용 데이터베이스인 KCI에서는 이러한 연구가 현재까지는 이루어지지 않은 것으로 알려져 있다. 따라서 본 연구에서는 기존의 여러 가지 네트워크 군집화 알고리즘을 이용하여 KCI에 등재되어 있는 자연과학 분야 학술지를 대상으로 인용관계에 따른 공동체를 파악하고 KCI에 등록된 학술지 분류와 비교하여 보았다. 적용된 군집화 방법 중 인포맵 알고리즘에 의한 분류가 KCI 등재 자연과학 분야 학술지의 인용관계 구조를 잘 반영하며, 기존의 KCI 분류와 가장 유사한 것으로 나타났다. 본 연구를 통해 얻은 KCI의 기존 분류와 차이점들은 장차 KCI 학술지의 재분류가 이루어질 시 고려의 대상이 될 수도 있을 것이다.

Keywords

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