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Scientometrics

, Volume 90, Issue 3, pp 1015–1026 | Cite as

The organization of scientific knowledge: the structural characteristics of keyword networks

  • Sangyoon Yi
  • Jinho ChoiEmail author
Article

Abstract

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.

Keywords

Organization of knowledge Keyword network Small-world network Power-law distribution Hierarchy 

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MEST) (No. 2009-0070359).

References

  1. Abrahamson, E. (1996). Management fashion. Academy of Management Review, 21(1), 254–285.Google Scholar
  2. Baldwin, C. Y., & Clark, K. B. (2000). Design rules: the power of modularity. MA: Cambridge.Google Scholar
  3. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.MathSciNetCrossRefGoogle Scholar
  4. Barnett, G. A., Huh, C., Kim, Y., & Park, H. W. (2011). Citations among communication journals and other disciplines: a network analysis. Scientometrics, 88(2), 449–469.CrossRefGoogle Scholar
  5. Crane, D. (1972). Invisible colleges: diffusion of knowledge in scientific communities. Chicago and London: The University of Chicago Press.Google Scholar
  6. Derényi, I., Palla, G., & Vicsek, T. (2005). Clique percolation in random networks. Physical Review Letters, 94(16), 160202.CrossRefGoogle Scholar
  7. Ebel, H., Davidsen, J., & Bornholdt, S. (2002). Dynamics of social networks. Complexity, 8(2), 24–27. doi: 10.1002/cplx.10066.MathSciNetCrossRefGoogle Scholar
  8. Ethiraj, S. K., & Levinthal, D. (2004). Bounded rationality and the search for organizational architecture: An evolutionary perspective on the design of organizations and their evolvability. Administrative Science Quarterly, 49(3), 404–437.Google Scholar
  9. Fleming, L., & Sorenson, O. (2001). Technology as a complex adaptive system: evidence from patent data. Research Policy, 30(7), 1019–1039.CrossRefGoogle Scholar
  10. Granovetter, M. (1973). Strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.CrossRefGoogle Scholar
  11. Hung, S. W., & Wang, A. P. (2010). Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network. Scientometrics, 82(1), 121–134.CrossRefGoogle Scholar
  12. Kuhn, T. (1962). The structure of scientific revolutions. Chicago: The University of Chicago Press.Google Scholar
  13. Lee, P.-C., Su, H.-N., & Chan, T.-Y. (2010). Assessment of ontology-based knowledge network formation by Vector-Space Model. Scientometrics, 85(3), 689–703.CrossRefGoogle Scholar
  14. Li, X., Chen, H., Huang, Z., & Roco, M. C. (2007). Patent citation network in nanotechnology (1976–2004). Journal of Nanoparticle Research, 9, 337–352.CrossRefGoogle Scholar
  15. McCloskey, D. N. (1998). The rhetoric of economics. Wisconsin: University of Wisconsin Press.Google Scholar
  16. McGrath, W. E. (1996). The unit of analysis (objects of study) in bibliometrics and scientometrics. Scientometrics, 35(2), 257–264.CrossRefGoogle Scholar
  17. Milgram, S. (1967). The small world problem. Psychology Today, 1, 61–67.Google Scholar
  18. Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46(5), 323–351.CrossRefGoogle Scholar
  19. Palla, G., Barabasi, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.CrossRefGoogle Scholar
  20. Palla, G., Derenyi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814–818.CrossRefGoogle Scholar
  21. Plotkin, H. C. (1997). Darwin machines and the nature of knowledge. MA: Harvard University Press.Google Scholar
  22. Price, D. J. D. (1965). Networks of scientific papers. Science, 149(3683), 510–515.CrossRefGoogle Scholar
  23. Ravasz, E., & Barabasi, A. L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112.CrossRefGoogle Scholar
  24. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabasi, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297(5586), 1551–1555.CrossRefGoogle Scholar
  25. Sanchez, R., & Mahoney, J. T. (1996). Modularity, flexibility, and knowledge management in product and organization design. Strategic Management Journal, 17, 63–76.Google Scholar
  26. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.Google Scholar
  27. Solé, R. V., Corominas-Murtra, B., Valverde, S., & Steels, L. (2010). Language networks: their structure, function, and evolution. Complexity, 15(6), 20–26.CrossRefGoogle Scholar
  28. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.CrossRefGoogle Scholar
  29. Su, H.-N., & Lee, P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79.CrossRefGoogle Scholar
  30. Thompson, J. D. (1967). Organizations in action. New York: McGraw-Hill.Google Scholar
  31. Trajtenberg, M. (1990). A penny for your quotes: patent citations and the value of innovations. RAND Journal of Economics, 21(1), 172–187.CrossRefGoogle Scholar
  32. Upham, S., Rosenkopf, L., & Ungar, L. (2010). Positioning knowledge: schools of thought and new knowledge creation. Scientometrics, 83(2), 555–581.CrossRefGoogle Scholar
  33. Valverde, S., Sole, R. V., Bedau, M. A., & Packard, N. (2007). Topology and evolution of technology innovation networks. Physical Review E, 76, 56–118.Google Scholar
  34. Watts, D. J. (1999). Small worlds: the dynamics of networks between order and randomness. Princeton: Princeton University Press.Google Scholar
  35. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  36. Weick, K. E. (1976). Educational organizations as loosely coupled systems. Administrative Science Quarterly, 21(1), 1–19.CrossRefGoogle Scholar
  37. Wright, S. (1932). The Roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the VI International Congress of Genetics, 356–366.Google Scholar
  38. Wright, S. (1964). Stochastic processes in evolution. In J. Gurland (Ed.), Stochastic models in medicine and biology (pp. 199–241). USA: University of Wisconsin Press.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.Department of Marketing and ManagementUniversity of Southern DenmarkOdense MDenmark
  2. 2.School of BusinessSejong UniversitySeoulKorea

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