Journal List > J Korean Neuropsychiatr Assoc > v.54(4) > 1017757

Choi and Gim: The Characteristic Analysis of Researches Network for Journal of Korean Neuropsychiatric Association

Abstract

Objectives

This study evaluated the structural characteristics of a scientific network of psychiatry and the effect of social networks on the performance of scholars.

Methods

The data were extracted from 261 articles published from 1996 to 2013 in the Journal of the Korean Neuropsychiatric Association, and were transformed into a co-author and their affiliation matrix. We used measures from network analysis (i.e., degree centrality, weighted degree centrality, eigenvector centrality, betweenness centrality) for evaluating the effect of co-authorship network on the performance of scholars (h-index). Netminer 4.1 was used for the network analysis.

Results

Both co-authorship and affiliation network demonstrated power law distribution. Coauthor's centralities were correlated with research achievements. Results from poisson regression analysis showed that the eigenvector centrality has a significant positive influence on the h-index and the weighted degree centrality has a significant negative influence on the h-index.

Conclusion

This study shows that the small world phenomenon exists in the psychiatric coauthorship network, and finds collaboration patterns and effects on scientific performance. The results suggest that in order to achieve better research performance it would be helpful for scholars to work with other well-performing scholars and avoid other scholars who previously worked together.

Figures and Tables

Fig. 1

Co-authorship network in psychiatry in Korea from 2009 to 2013. Each node represents one author in the data. The size of each node is proportional to the eigenvector centrality. Color shifts from red to purple indicate h-index of each node from lower to larger, respectively. Each link between the nodes indicates their connection.

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Fig. 2

Affiliation network in psychiatry in Korea from 2009 to 2013. Each node represents one affiliation. The size of each node is proportional to the eigenvector centrality. Color shifts from red to purple indicate degree centrality of each node from lower to larger, respectively. Each link between the nodes indicates their connection.

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Table 1

Co-authorship network and affiliation network metrics

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Table 2

Name, DC, WDC, CC, EC, BC, and h-index of the top 40 performing researchers

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DC : Degree centrality, WDC : Weighted degree centrality, CC : Closeness centrality, EC : Eigenvector centrality, BC : Betweenness centrality

Table 3

Spearman rank correlation matrix for a variables

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* : p<0.01

Table 4

Poisson multiple regression results for five independent variables and the h-index as dependent variable

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SE : Standard error

Table 5

Top twenty affiliations : DC, WDC, CC, EC, and BC

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DC : Degree centrality, WDC : Weighted degree centrality, CC : Closeness centrality, EC : Eigenvector centrality, BC : Betweenness centrality, U : University, H : Hospital

Notes

Conflicts of Interest The authors have no financial conflicts of interest.

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