Journal of the Korean Society for Library and Information Science (한국문헌정보학회지)

Korean Society For Library And Information Science (한국문헌정보학회)
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A Study on the Network Generation Methods for Examining the Intellectual Structure of Knowledge Domains

지적 구조의 규명을 위한 네트워크 형성 방식에 관한 연구

  • Published : 2006.06.01
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Abstract

Network generation methods to visualize bibliometric data for examining the intellectual structure of knowledge domains are investigated in some detail. Among the four methods investigated in this study, pathfinder network algorithm is the most effective method in representing local details as well as global intellectual structure. The nearest neighbor graph, although never used in bibliometic analysis, also has some advantages such as its simplicity and clustering ability. The effect of input data preparation process on resulting intellectual structures are examined, and concluded that unlike MDS map with clusters, the network structure could be changed significantly by the differences in data matrix preparation process. The network generation methods investigated in this paper could be alternatives to conventional multivariate analysis methods and could facilitate our research on examining intellectual structure of knowledge domains.

이 연구에서는 지적 구조 분석을 위해서 계량서지적 자료를 시각적으로 표현하는 다양한 네트워크 형성 방식에 대해서 사례와 함께 각각의 특성을 살펴보았다. 기준값 절단 방식, 최근접이웃 그래프, 최소비용 신장트리, 패스파인더 네트워크의 네 가지 네트워크 형성 방식 중에서 전체 구조와 세부 구조의 표현 능력이 모두 뛰어난 패스파인더 네트워크 알고리즘이 최근 가장 활발히 응용되고 있다. 최근접이웃 그래프는 아직까지 계량서지적 분석에 응용된 사례는 없으나 간단한 알고리즘과 클러스터링 능력 등과 같은 지적 구조 규명에 도움이 될 수 있는 몇 가지 장점을 갖추고 있는 것으로 확인되었다. 다차원척도나 군집분석과 달리 네트워크를 이용한 시각화에서는 입력자료의 전처리에 따라서 생성된 지적 구조의 차이가 큰 것으로 나타났다. 이 연구에서 고찰한 여러 네트워크 형성 방식을 적절히 활용함으로써 국내의 지적 구조 규명 연구를 활성화할 수 있을 것이라 기대된다.

Keywords

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