Skip to main content

Finding Cross-Border Collaborative Centres in Biopharma Patent Networks: A Clustering Comparison Approach Based on Adjusted Mutual Information

Part of the Studies in Computational Intelligence book series (SCI,volume 1015)

Abstract

The recent speedy development of COVID-19 mRNA vaccines has underlined the importance of cross-border patent collaboration. This paper uses the latest edition of the REGPAT database from the OECD and constructs the co-applicant patent networks for the fields of biotechnology and pharmaceuticals. We identify the cross-border collaborative regional centres in these patent networks at NUTS3 level using a clustering comparison approach based on adjusted mutual information (AMI). In particular, we measure and compare the AMI scores of the clustering before and after arbitrarily removing cross-border links of a focal node against the default clustering defined by national borders. The region with the largest difference in AMI scores is identified as the most cross-border collaborative centre, hence the name of our measure, AMI gain. We find that our measure both correlates with and has advantages over the traditional measure betweenness centrality and a simple measure of foreign share.

Keywords

  • Patent networks
  • Clustering comparison
  • Adjusted mutual information
  • Cross-border

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-93409-5_6
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   309.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-93409-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   399.99
Price excludes VAT (USA)
Hardcover Book
USD   399.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Jaffe, A.B., Trajtenberg, M., Henderson, R.: Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 108(3), 577–598 (1993)

    CrossRef  Google Scholar 

  2. Kogut, B., Zander, U.: Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3(3), 383–397 (1992)

    CrossRef  Google Scholar 

  3. Koschatzky, K., Sternberg, R.: R&D cooperation in innovation systems-some lessons from the European regional innovation survey (ERIS). Eur. Plan. Stud. 8(4), 487–501 (2000)

    CrossRef  Google Scholar 

  4. Organisation for Economic Co-operation and Development (OECD). Managing national innovation systems. OECD Publishing (1999)

    Google Scholar 

  5. Chang, P.-L., Shih, H.-Y.: The innovation systems of Taiwan and China: a comparative analysis. Technovation 24(7), 529–539 (2004)

    CrossRef  Google Scholar 

  6. Gaviria, M., Kilic, B.: A network analysis of COVID-19 mRNA vaccine patents. Nat. Biotechnol. 39(5), 546–548 (2021)

    CrossRef  Google Scholar 

  7. Griliches, Z., Pakes, A., Hall, B.H.: The value of patents as indicators of inventive activity (1986)

    Google Scholar 

  8. Fleming, L.: Recombinant uncertainty in technological search. Manag. Sci. 47(1), 117–132 (2001)

    CrossRef  Google Scholar 

  9. Jaffe, A.B., Trajtenberg, M.: Patents, Citations, and Innovations: A Window on the Knowledge Economy. MIT Press, Cambridge (2002)

    Google Scholar 

  10. Hall, B.H., Jaffe, A., Trajtenberg, M.: Market value and patent citations. RAND J. Econ. 36, 16–38 (2005)

    Google Scholar 

  11. Gao, Y., Zhu, Z., Riccaboni, M.: Consistency and trends of technological innovations: a network approach to the international patent classification data. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds.) COMPLEX NETWORKS 2017 2017. SCI, vol. 689, pp. 744–756. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72150-7_60

    CrossRef  Google Scholar 

  12. Gao, Y., Zhu, Z., Kali, R., Riccaboni, M.: Community evolution in patent networks: technological change and network dynamics. Appl. Netw. Sci. 3(1), 1–23 (2018). https://doi.org/10.1007/s41109-018-0090-3

    CrossRef  Google Scholar 

  13. Maraut, S., Dernis, H., Webb, C., Spiezia, V., Guellec, D.: The OECD REGPAT database: a presentation. OECD Sci. Technol. Ind. Work. Pap. 2008(2), 0_1 (2008)

    Google Scholar 

  14. Singh, J.: Collaborative networks as determinants of knowledge diffusion patterns. Manag. Sci. 51(5), 756–770 (2005)

    CrossRef  MATH  Google Scholar 

  15. Morescalchi, A., Pammolli, F., Penner, O., Petersen, A.M., Riccaboni, M.: The evolution of networks of innovators within and across borders: evidence from patent data. Res. Policy 44(3), 651–668 (2015)

    CrossRef  Google Scholar 

  16. Sebestyén, T., Varga, A.: Research productivity and the quality of interregional knowledge networks. Ann. Reg. Sci. 51(1), 155–189 (2013)

    CrossRef  Google Scholar 

  17. De Noni, I., Orsi, L., Belussi, F.: The role of collaborative networks in supporting the innovation performances of lagging-behind European regions. Res. Policy 47(1), 1–13 (2018)

    CrossRef  Google Scholar 

  18. Wanzenboeck, I., Scherngell, T., Brenner, T.: Embeddedness of regions in European knowledge networks: a comparative analysis of inter-regional R&D collaborations, co-patents and co-publications. Ann. Reg. Sci. 53(2), 337–368 (2014)

    CrossRef  Google Scholar 

  19. Wanzenböck, I., Scherngell, T., Lata, R.: Embeddedness of European regions in European union-funded research and development (R&D) networks: a spatial econometric perspective. Reg. Stud. 49(10), 1685–1705 (2015)

    CrossRef  Google Scholar 

  20. Bergé, L.R., Wanzenböck, I., Scherngell, T.: Centrality of regions in R&D networks: a new measurement approach using the concept of bridging paths. Reg. Stud. 51(8), 1165–1178 (2017)

    CrossRef  Google Scholar 

  21. Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)

    MathSciNet  MATH  Google Scholar 

  22. WIPO: IPC concordance table (2019). https://www.wipo.int/ipstats. Accessed 06 Aug 2021

  23. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)

    CrossRef  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhen Zhu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Zhu, Z., Gao, Y. (2022). Finding Cross-Border Collaborative Centres in Biopharma Patent Networks: A Clustering Comparison Approach Based on Adjusted Mutual Information. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93409-5_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93408-8

  • Online ISBN: 978-3-030-93409-5

  • eBook Packages: EngineeringEngineering (R0)