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Evolution and structure of scientific co-publishing network in Korea between 1948–2011

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This study investigates the evolution and structure of a national-scale co-publishing network in Korea from 1948 to 2011. We analyzed more than 700,000 papers published by approximately 415,000 authors for temporal changes in productivity and network properties with a yearly resolution. The resulting statistical properties were compared to findings from previous studies of coauthorship networks at the national and discipline levels. Our results show that both the numbers of publications and authors in Korea have grown exponentially in a 64 year time frame. Korean scholars have become more productive and collaborative. They now form a small-world-ish network where most authors can connect with one other within an average of 5.33 degrees of separation. The increasingly skewed distribution and concentration of both productivity and the number of collaborators per author indicate that a relatively small group of individuals have accumulated a large number of opportunities for co-publishing. This implies a potential vulnerability for the network and its wider context: the graph would disintegrate into a multitude of smaller components, where the largest one would contain <2 % of all authors, if approximately 15 % (57,724) of the most connected scholars left the network, e.g., due to retirement or promotion to higher-level administrative positions.

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  1. 1.

    More than 97 % of the authors identified were found to be associated with universities, institutes, or companies in Korea. Although there might be scholars who reside overseas and may participate in papers published in Korea, we assume that that does not affect our study.

  2. 2.

    In our study, a power law distribution is fitted to the observed degree distribution using the maximum-likelihood fitting method with the goodness-of-fit test based on the Kolmogorov–Smirnov statistic as described in Clauset et al. (2009).

  3. 3.

    A graph-level Watts–Strogatz clustering coefficient is the average of the ego network densities of all authors.

  4. 4.

    The coauthorship networks in our study were constructed by converting an author-by-paper matrix (i.e., two-mode or bipartite network) into an author-by-author matrix (i.e., one-mode or monopartite network). Several scholars have proposed to compare bipartite graphs (e.g. networks of interlocking directors/companies or inventors/patents) against bipartite random graphs. For more details, please refer to Robins and Alexander (2004) or Kogut and Belinky (2008).

  5. 5.

    This interpretation needs to be taken with caution. Even though a path is relatively short, most potential within and across field collaborations will not be realized.

  6. 6.

    For example, one topic for further investigations might be to analyze whether the increased productivity and connectivity are due to new cohorts of scientists who truly are more productive than more established scholars, or because scholars who have been in a field for many years have accumulated a multitude of publications.


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This work is supported by KISTI (Korea Institute of Science and Technology Information), grant P14033. The American Physical Society (APS, http://journals.aps.org/datasets) kindly provided the publication records of the Physical Review journals for our research. We would like to thank Brian Karrer (Facebook), Travis Martin (University of Michigan), Brian Ball (Dotomi Inc.) and Mark E. J. Newman (University of Michigan) for helping us to disambiguate author names in the APS dataset. We also thank the anonymous reviewers who helped us improve the quality of this paper, and Susan Lafferty (GSLIS, University of Illinois at Urbana-Champaign) for editing the manuscript.

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Correspondence to Jinseok Kim.

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Kim, J., Tao, L., Lee, S. et al. Evolution and structure of scientific co-publishing network in Korea between 1948–2011. Scientometrics 107, 27–41 (2016). https://doi.org/10.1007/s11192-016-1878-5

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  • Bibliometrics
  • Coauthorship networks
  • Authority control
  • Network evolution
  • Small-world networks