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Modeling innovation by a kinetic description of the patent citation system

Author

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  • Csárdi, Gábor
  • Strandburg, Katherine J.
  • Zalányi, László
  • Tobochnik, Jan
  • Érdi, Péter

Abstract

This paper reports results of a network theory approach to the study of the United States patent system. We model the patent citation network as a discrete time, discrete space stochastic dynamic system. From patent data we extract an attractiveness function, A(k,l), which determines the likelihood that a patent will be cited. A(k,l) shows power law aging and preferential attachment. The exponent of the latter is increasing since 1993, suggesting that patent citations are increasingly concentrated on a relatively small number of patents. In particular, our results appear consistent with an increasing patent “thicket”, in which more and more patents are issued on minor technical advances.

Suggested Citation

  • Csárdi, Gábor & Strandburg, Katherine J. & Zalányi, László & Tobochnik, Jan & Érdi, Péter, 2007. "Modeling innovation by a kinetic description of the patent citation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(2), pages 783-793.
    • Handle: RePEc:eee:phsmap:v:374:y:2007:i:2:p:783-793
    DOI: 10.1016/j.physa.2006.08.022
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    References listed on IDEAS

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    1. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    2. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    3. Bronwyn H. Hall, 2005. "Exploring the Patent Explosion," The Journal of Technology Transfer, Springer, vol. 30(2_2), pages 35-48, January.
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    Citations

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    Cited by:

    1. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
    2. Sheridan, Paul & Yagahara, Yuichi & Shimodaira, Hidetoshi, 2012. "Measuring preferential attachment in growing networks with missing-timelines using Markov chain Monte Carlo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 5031-5040.
    3. Tamar C Weenen & Bahar Ramezanpour & Esther S Pronker & Harry Commandeur & Eric Claassen, 2013. "Food-Pharma Convergence in Medical Nutrition– Best of Both Worlds?," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
    4. Chunjuan Luan & Haiyan Hou & Yongtao Wang & Xianwen Wang, 2014. "Are significant inventions more diversified?," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 459-470, August.
    5. Inoue, Masaaki & Pham, Thong & Shimodaira, Hidetoshi, 2020. "Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks," Journal of Informetrics, Elsevier, vol. 14(3).

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