초록

Abstract

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

초록

이 연구에서는 연구자의 연구 이력을 분석하기 위해서 White(2000)가 제안한 인용 정체성과 Hellsten 등(2007)이 제안한 자기 인용 네트워크의 두 가지 최신 기법을 비교해보았다. 국내 대표적인 정보학자인 정영미의 연구 성과물을 대상으로 실험적인 분석을 수행해본 결과 두 기법에서 구분한 연구 시기가 동일하게 나뉘었으며 주요 연구 주제도 유사하게 파악되었다. 그러나 인용 정체성 지도에서는 주제영역별로 영향받은 주요 저자를 파악할 수 있는 반면에 자기 인용 네트워크에서는 시기별 핵심 문헌과 선도 문헌이 식별되었다. 따라서 이 두 가지 기법을 상호보완적으로 사용할 때 연구자의 연구 이력에 대해서 풍부한 정보를 획득할 수 있다는 결론을 얻었다.

Abstract

This paper compares two recent methods for exploring a scientist's research history: citation identity and self-citation network. The former is proposed by White(2000), while the latter is suggested by Hellsten et al.(2007). An experimental citation analysis was carried out on the research output of Young Mee Chung, a renouned Korean information scientist. The result shows that the two methods divided the research period into two sub-periods in the same way. They also identified the major research themes very similarly. In the analysis of each method's performance in depth, the two methods revealed different functions to understand a researcher's history. Citation identity was useful to identify authors who have affected Chung's research in terms of research topics. whereas, self-citation network was successful to identify the core papers and leading papers of the research sub-periods. This study indicates the combination of two methods can provide rich information on a scientist's research history.