A Study on Document Citation Indicators Based on Citation Network Analysis

이재윤,

doi:10.4275/kslis.2011.45.2.119

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…Others such as bounded-distance betweenness [9], distance-scaled betweenness [9], edge betweenness [11] and group betweenness [12] are also introduced. In [18], the authors used a metric called the "complex degree centrality" [20] for a coauthor network and observed this centrality to exhibit a correlation coefficient of 0.848 with the node betweenness metric. The complex degree centrality of a vertex (author) is computed as an entropy-based measure of the number of co-authors and the number of co-authored papers for the author.…”

Section: Related Workmentioning

confidence: 99%

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2016

IJFCST

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In this paper, we seek to find a computationally light centrality metric that could serve as an alternate for the computationally heavy betweenness centrality (BWC) metric. In this pursuit, in the first half of the paper, we evaluate the correlation coefficient between BWC and the other commonly used centrality metrics such as Degree Centrality (DEG) , Closeness Centrality (CLC), Farness Centrality (FRC), Clustering Coefficient Centrality (CCC) and Eigenvector Centrality (EVC). We observe BWC to be highly correlated with DEG for synthetic networks generated based on the Erdos-Renyi model (for random networks) and Watts-Strogatz model (for small-world networks). In the second half of the paper, we conduct a regression analysis for BWC with that of a recently proposed centrality metric called the localized clustering coefficient complement-based degree centrality (LCC'DC) for a suite of 47 real-world networks. The R-Squared metric and Correlation coefficient for the LCC'DC-BWC regression has been observed to be appreciably greater than those observed for the DEG-BWC regression. We also observe the LCC'DC-BWC regression to incur relatively a lower value for the standard error of residuals for a majority of the real-world networks.

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Section: Related Workmentioning

confidence: 99%

Untitled

2016

IJFCST

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“…The studies that compare various indicators to find an effective indicator have increased (Bollen, Van de Sompel, Hagberg, & Chute, 2009;Bornmann, Mutz, Hug, & Daniel, 2011;Costas, van Leeuwen, & Bordons, 2010;Kim & Lee, 2010;Lee, 2011;Leydesdorff, 2009;Yu & Lee, 2008). The analysis methods are correlation analysis, factor analysis, or MDS.…”

Section: Previous Researchmentioning

confidence: 99%

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

Yu¹

2012

Journal of the Korean Society for information Management

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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.

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Network analysis using frequency of cross-citation and comparing citation index of accounting journals

Choi

Oh²,

Kwag³

2014

Journal of Digital Convergence

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Currently, despite the quantitative growth of domestic journal, objective and metric criterion to evaluate the contents and quality of those journals is inadequate. As a result, depending on a foreign citation index like SCI to evaluate the research result is causing some problems such as, thesis of an excellent standard being presented and published in foreign countries, thus, neglecting journals written in Korean. Hereat, the Korea Citation Index(KCI) is constructing a database of the journals published by academic associations; scientific paper information, articles information, and bibliographic, analyzing the quoting relation. Consequently, KCI is providing the basic information and the corresponding quoting relations of journals and papers on its webpage. This study compares the impact level of domestic accounting related journals by KCI and analyzes the network among journals using the cross-citation frequency from a social network analysis point of view. As a result, Accounting-related journals are now more desirable than the location in order to have a variety of interdisciplinary research needs to be encouraged. Through this exchange of other disciplines and actively made, which is the phase of apparent accounting-related journals expect to be greatly enhanced looks.

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A Study on Document Citation Indicators Based on Citation Network Analysis

Cited by 11 publications

References 27 publications

Untitled

Untitled

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

Network analysis using frequency of cross-citation and comparing citation index of accounting journals

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