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Scientometrics

, Volume 107, Issue 1, pp 27–41 | Cite as

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

  • Jinseok Kim
  • Liang Tao
  • Seok-Hyoung Lee
  • Jana Diesner
Article

Abstract

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.

Keywords

Bibliometrics Coauthorship networks Authority control Network evolution Small-world networks 

Notes

Acknowledgments

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.

References

  1. Abramo, G., D’Angelo, C. A., & Murgia, G. (2013). The collaboration behaviors of scientists in Italy: A field level analysis. Journal of Informetrics, 7(2), 442–454. doi: 10.1016/j.joi.2013.01.009.CrossRefGoogle Scholar
  2. Abt, H. A. (2007). The future of single-authored papers. Scientometrics, 73(3), 353–358. doi: 10.1007/s11192-007-1822-9.CrossRefGoogle Scholar
  3. Barabási, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A—Statistical Mechanics and Its Applications, 311(3–4), 590–614. doi: 10.1016/s0378-4371(02)00736-7.MathSciNetCrossRefMATHGoogle Scholar
  4. Börner, K., Maru, J. T., & Goldstone, R. L. (2004). The simultaneous evolution of author and paper networks. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl. 1), 5266–5273. doi: 10.1073/pnas.0307625100.CrossRefGoogle Scholar
  5. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.CrossRefMATHGoogle Scholar
  6. Çavuşoğlu, A., & Türker, İ. (2013). Scientific collaboration network of Turkey. Chaos, Solitons and Fractals, 57, 9–18.CrossRefGoogle Scholar
  7. Chen, Z. F., & Guan, J. C. (2010). The impact of small world on innovation: An empirical study of 16 countries. Journal of Informetrics, 4(1), 97–106. doi: 10.1016/j.joi.2009.09.003.CrossRefGoogle Scholar
  8. Choi, S.-H., Kim, B.-K., Kang, M., You, B.-J., Lee, J., & Park, J.-W. (2011). A study of citing patterns of Korean scientists on Korean journals. Journal of the Korean Society for Information Management, 28(2), 97–115.CrossRefGoogle Scholar
  9. Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661–703.MathSciNetCrossRefMATHGoogle Scholar
  10. Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8), 1557–1575. doi: 10.1016/j.jedc.2003.04.002.MathSciNetCrossRefMATHGoogle Scholar
  11. Fegley, B. D., & Torvik, V. I. (2013). Has large-scale named-entity network analysis been resting on a flawed assumption? PLoS ONE, 8(7), 1–16. doi: 10.1371/journal.pone.0070299.CrossRefGoogle Scholar
  12. Franceschet, M. (2011). Collaboration in computer science: A network science approach. Journal of the American Society for Information Science and Technology, 62(10), 1992–2012. doi: 10.1002/asi.21614.CrossRefGoogle Scholar
  13. Glasser, G. J. (1962). Variance formulas for the mean difference and coefficient of concentration. Journal of the American Statistical Association, 57(299), 648–654. doi: 10.2307/2282402.MathSciNetCrossRefMATHGoogle Scholar
  14. Gossart, C., & Özman, M. (2009). Co-authorship networks in social sciences: The case of Turkey. Scientometrics, 78(2), 323–345. doi: 10.1007/s11192-007-1963-x.CrossRefGoogle Scholar
  15. Grossman, J. W. (2002). Patterns of collaboration in mathematical research. SIAM News, 35(9), 8–9.Google Scholar
  16. Han, S. H., Cho, S. R., Yang, J. M., & Ryu, D. A. (2009). A study on academic research and development activities in Korea. Seoul: National Research Foundation of Korea.Google Scholar
  17. Kim, M.-J. (2005). Korean science and international collaboration, 1995–2000. Scientometrics, 63(2), 321–339.CrossRefGoogle Scholar
  18. Kim, J., & Diesner, J. (2015). The effect of data pre-processing on understanding the evolution of collaboration networks. Journal of Informetrics, 9(1), 226–236. doi: 10.1016/j.joi.2015.01.002.CrossRefGoogle Scholar
  19. Kim, J., Kim, H., & Diesner, J. (2014). The impact of name ambiguity on properties of coauthorship networks. Journal of Information Science Theory and Practice, 2(2), 6–15. doi: 10.1633/JISTaP.2014.2.2.1.CrossRefGoogle Scholar
  20. Kogut, B., & Belinky, M. (2008). Comparing small world statistics over time and across countries: An introduction to the special issue comparative and transnational corporate networks. European Management Review, 5(1), 1–10. doi: 10.1057/emr.2008.6.CrossRefGoogle Scholar
  21. Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031. doi: 10.1002/asi.20591.CrossRefGoogle Scholar
  22. Lužar, B., Levnajić, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in Slovenia’s scientific collaboration network. PLoS ONE, 9(4), e94429. doi: 10.1371/journal.pone.0094429.CrossRefGoogle Scholar
  23. Martin, T., Ball, B., Karrer, B., & Newman, M. E. J. (2013). Coauthorship and citation patterns in the physical review. Physical Review E, 88(1), 012814-1–012814-9. doi: 10.1103/PhysRevE.88.012814.CrossRefGoogle Scholar
  24. Merton, R. K. (1968). Matthew effect in science. Science, 159(3810), 56. doi: 10.1126/science.159.3810.56.CrossRefGoogle Scholar
  25. Ministry of Education. (2013). Statistical yearbook of education. Seoul, Korea: Ministry of Education.Google Scholar
  26. Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69(2), 213–238.CrossRefGoogle Scholar
  27. Newman, M. E. J. (2000). Models of the small world. Journal of Statistical Physics, 101(3–4), 819–841. doi: 10.1023/A:1026485807148.CrossRefMATHGoogle Scholar
  28. Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409. doi: 10.1073/pnas.021544898.MathSciNetCrossRefMATHGoogle Scholar
  29. Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl. 1), 5200–5205. doi: 10.1073/pnas.0307545100.CrossRefGoogle Scholar
  30. Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.CrossRefMATHGoogle Scholar
  31. Park, H. W., & Leydesdorff, L. (2008). Korean journals in the Science Citation Index: What do they reveal about the intellectual structure of S&T in Korea? Scientometrics, 75(3), 439–462.CrossRefGoogle Scholar
  32. Perc, M. (2010). Growth and structure of Slovenia’s scientific collaboration network. Journal of Informetrics, 4(4), 475–482.MathSciNetCrossRefGoogle Scholar
  33. Robins, G., & Alexander, M. (2004). Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Computational and Mathematical Organization Theory, 10(1), 69–94. doi: 10.1023/B:CMOT.0000032580.12184.c0.CrossRefMATHGoogle Scholar
  34. Ryoo, J. W. (2011). The labor market for college professors in Korea. Korean Journal of Labor Economics, 34(2), 1–27.Google Scholar
  35. Schubert, A., & Glanzel, W. (2006). Cross-national preference in co-authorship, references and citations. Scientometrics, 69(2), 409–428. doi: 10.1007/s11192-006-0160-7.CrossRefGoogle Scholar
  36. Strotmann, A., & Zhao, D. (2012). Author name disambiguation: What difference does it make in author-based citation analysis? Journal of the American Society for Information Science and Technology, 63(9), 1820–1833. doi: 10.1002/Asi.22695.CrossRefGoogle Scholar
  37. Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. Acm Transactions on Knowledge Discovery from Data, 3(3), 1–29. doi: 10.1145/1552303.1552304.CrossRefGoogle Scholar
  38. Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small world Problem1. American Journal of Sociology, 111(2), 447–504.CrossRefGoogle Scholar
  39. Waltman, L. (2012). An empirical analysis of the use of alphabetical authorship in scientific publishing. Journal of Informetrics, 6(4), 700–711. doi: 10.1016/j.joi.2012.07.008.MathSciNetCrossRefGoogle Scholar
  40. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  41. Yoshikane, F., & Kageura, K. (2004). Comparative analysis of coauthorship networks of different domains: The growth and change of networks. Scientometrics, 60(3), 435–446. doi: 10.1023/b:scie.0000034385.05897.46.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Graduate School of Library and Information ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Department of Agricultural EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Department of Overseas InformationKorea Institute of Science and Technology InformationYuseong-Gu, DaejeonKorea

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