JACIII Vol.15 No.2 pp. 180-187
doi: 10.20965/jaciii.2011.p0180


Analyses of Compound Structures of Groups that Produce Intellectual Property

Hiroyasu Inoue

Osaka Sangyo University, 3-1-1 Nakagaito, Daito-shi, Osaka 574-0013, Japan

August 4, 2010
December 31, 2010
March 20, 2011
network, patent, paper, joint application, joint authorship

This paper focuses on collaborations between scientists and engineers and investigates their mutual benefits. More concretely, multi-layered networks separated into four scientific/technological areas are investigated. The areas are life sciences (Bio), nanotechnology/materials (Nano), information and telecommunications (IT), and environmental sciences (Env), and they are mentioned in the third science and technology basic plan issued by the Government of Japan. The networks were then analyzed by using p* models to find compound structures. Logistic regression analysis was conducted, and the compound structures were expressed by explanatory variables. In all four areas, joint authorship and joint application tend to overlap. A role interlocking structure is only found in Bio, and itmeans that a gatekeeper exists between scientific knowledge and technical knowledge. A transitivity structuremeans three-person groups emerge such that a central person publishes papers (or patents) with two other people, and the two other people publish the other outcomes, and patents (or papers). It is found that transitivity is generally not reversible. In Bio and Nano, there is no eminent difference in significance of the two different types of transitivity, but in IT and Env, segregations with a joint application expert and joint authorship support emerge more strongly than the other types of segregations.

Cite this article as:
H. Inoue, “Analyses of Compound Structures of Groups that Produce Intellectual Property,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 180-187, 2011.
Data files:
  1. [1] J. A. Schumpeter, “Theorie der wirtschaftlichen Entwicklung,” Duncker & Humblot Gmbhk, 1912.
  2. [2] E. Mansfield, “Academic Research and Industrial Innovation,” Research Policy, Vol.20, No.1, 1991.
  3. [3] H. W. Chesbrough, “Open innovation,” Harvard Business School, 2003.
  4. [4] Government of Japan, “Science and Technology Basic Plan,” 2006.
  5. [5] K. W. Boyack and R. Klavans, “Measuring science-technology interaction using rare inventor-author names,” J. of Informetrics, Vol.2, pp. 173-182, 2008.
  6. [6] M. Meyer, “Are patenting scientists the better scholars? An exploratory comparison of inventor-authors with their non-inventing peers in nano-science and technology,” Research Policy, Vol.35, pp. 1646-1662, 2006.
  7. [7] F.Murray, “The role of academic inventors in entrepreneurial firms: sharing the laboratory life,” Research Policy, Vol.33, pp. 643-659, 2004.
  8. [8] S. Breschi and C. Catalini, “Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks,” Research Policy, Vol.39, pp. 14-26, 2010.
  9. [9] S. Tamada, Y. Naitou, F. Kodama, K. Gemba, and J. Suzuki, “Significant Difference of Dependence upon Scientific Knowledge among Different Technologies,” Scientometrics, Vol.68, No.2, pp. 289-302, 2006.
  10. [10] CiNii homepage,
  11. [11] P. J. Carrington, J. Scott, and S. Wasserman, “Models and Methods in Social Network Analysis,” Cambridge University Press, 2005.
  12. [12] L. M. Koehly and P. Pattison, “Random Graph Models for Social Networks: Multiple Relations or Multiple Raters,” P. J. Carrington, J. Scott, and S. Wasserman (Eds.), Models and Methods in Social Network Analysis, Chapter 9, Cambridge University Press, New York, 2005.
  13. [13] S. Wasserman, “Conformity of Two Sociometric Relations,” Psychometrika, Vol.52, pp. 3-18, 1987.
  14. [14] O. Frank and K. Nowicki, “Exploratory Statistical Analysis of Networks,” J. Gimbel, J. W. Kennedy, and L.V. Quintas (Eds.), Quo Vadis Graph Theory? A Source Book for Challenges and Directions, Amsterdam, 1993.
  15. [15] J. E. Besag, “Statistical Analysis of non-lattice data,” The Statistician, Vol.24, pp. 179-195, 1975.
  16. [16] J. E. Besag, “Some Methods of Statistical Analysis for Spatial Data,” Bulletin of the International Statistical Association, Vol.47, pp. 77-92, 1977.
  17. [17] D. Strauss and M. Ikeda, “Pseudolikelihood Estimation for Social Networks,” J. of the American Statistical Association, Vol.85, pp. 204-212, 1990.
  18. [18] P. Pattison and S. Wasserman, “Logit Models and Logistic Regressions for Social Networks: II. Multivariate Relations,” British J. of Mathematical and Statistical Psychology, Vol.52, pp. 169-193, 1999.

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