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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

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Vol.29 No.2

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Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2012, v.29 no.2, pp.193-204
https://doi.org/10.3743/KOSIM.2012.29.2.193

(2012). Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks. Journal of the Korean Society for Information Management, 29(2), 193-204, https://doi.org/10.3743/KOSIM.2012.29.2.193

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.

keywords
중심성, 동시인용, 인용 네트워크, 곡선 추정, 선형성, 비선형성, 정규 분포, centrality, co-citation, citation network, curve estimation, linearity, non-linearity, normal distribution, centrality, co-citation, citation network, curve estimation, linearity, non-linearity, normal distribution

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