Healthc Inform Res.  2010 Jun;16(2):100-119. 10.4258/hir.2010.16.2.100.

Analysis of Scientific Publication Networks among Medical Schools in Korea

• Kang JO
• Park SH

• KMID: 2166573
• DOI: http://doi.org/10.4258/hir.2010.16.2.100

Abstract

OBJECTIVES
This research was intended to analyze the special characteristics and structure of social networks among Korean medical schools for the purpose of providing knowledge regarding medical field structure, dynamics, and potential paradigm development.
METHODS
A collaborative 12-year data set of 35,469 published articles in the SCOPUS(R) database was analyzed. Among ISI subcategories, 61 having more than 20 articles were scrutinized. Following identification of correspondence and co-authorship, centralization indices and Key Player analysis were run for each subcategory. Medical schools were grouped into uniform clusters with convergence of iterated correlation (CONCOR) for structural equivalence. Finally, multidimensional scaling was used to visualize similarities.
RESULTS
All centralization indexes analyzed demonstrated a shift in the degree of centralization in the network of medical schools throughout the period examined. Betweenness centrality and eigenvector centrality in particular revealed a dramatic change indicating minimization of the role of a specific "gatekeeper". Key Player analysis confirmed Seoul National University as a constant 'key player' throughout the period evaluated and for the subcategories examined as well.
CONCLUSIONS
This study provided insight into the scientific network among the medical schools of Korea. By understanding this network, a strategy to strengthen the basis of research may be developed.

Keyword

Medical School; Network; Scientific Citation Index

MeSH Terms

Korea
Publications
Schools, Medical

Figure

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  Figure 1 Flow of data analysis.

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  Figure 2 Network centralization according to the period 1997-2000, 2001-2004, 2005-2008 and 2007-2008. The size of vertex (node) and the number of arcs are increased through the period so that the degree centrality was decreased.

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  Figure 3 Structural equivalence (convergence of iterated correlate [CONCOR]) generated eight groups from 40 medical schools in Korea. The medical schools of a group show similar pattern of behavior in SCIE publication.

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  Figure 4 Multidimensional scaling shows the similarities among medical schools. Through the period, only one medical school (Seonam, Korea) is outside the 1.5 metric (final stress = 0.391 after 62 iterations).

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