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Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data

Author

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  • Xipeng Liu

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Xinmiao Li

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

With the deterioration of the environment and the acceleration of resource consumption, green patent innovation focusing on environmental protection fields has become a research hot-spot around the world. Previous researchers constructed homogeneous information networks to analyze the influence of patents based on citation ranking algorithms. However, a patent information network is a complex network containing multiple pieces of information (e.g., citation, applicant, inventor), and the use of a single information network will result in incomplete information or information loss, and the obtained results are biased. In addition, scholars constructed centrality indicators to assess the importance of patents with less consideration of the age bias problem of algorithms and models, and the results obtained are inaccurate. In this paper, based on the Chinese green patent ( CNGP ) dataset from 1985 to 2020, a CNGP heterogeneous applicant-citation network is constructed, and the rescaling method and normalization procedure are used to solve the age bias. The results illustrate that the method proposed in this paper is able to identify significant patents earlier, and the performance of the rescaled indegree ( R_ID ) works best such as the IR score is 17.32% in the top 5% of the rankings, and it is the best in the constructed dynamic heterogeneous networks as well. In addition, the constructed heterogeneous information network has better results compared with the traditional homogeneous information network, such as the NIR score of R_ID metrics can be improved by 2% under the same condition. Therefore, the analysis method proposed in this paper can reasonably evaluate the quality of patents and identify significant patents earlier, thus providing a new method for scientists to measure the quality of patents.

Suggested Citation

  • Xipeng Liu & Xinmiao Li, 2022. "Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13870-:d:952936
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    as
    1. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    2. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    3. Petra Moser & Tom Nicholas, 2013. "Prizes, Publicity and Patents: Non-Monetary Awards as a Mechanism to Encourage Innovation," Journal of Industrial Economics, Wiley Blackwell, vol. 61(3), pages 763-788, September.
    4. Leonid Kogan & Dimitris Papanikolaou & Amit Seru & Noah Stoffman, 2017. "Technological Innovation, Resource Allocation, and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 665-712.
    5. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Zhang, Sufang & Andrews-Speed, Philip & Zhao, Xiaoli & He, Yongxiu, 2013. "Interactions between renewable energy policy and renewable energy industrial policy: A critical analysis of China's policy approach to renewable energies," Energy Policy, Elsevier, vol. 62(C), pages 342-353.
    7. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    8. Tan, Yongxian & Tian, Xuan & Zhang, Xinde & Zhao, Hailong, 2020. "The real effect of partial privatization on corporate innovation: Evidence from China's split share structure reform," Journal of Corporate Finance, Elsevier, vol. 64(C).
    9. Jevin D. West & Michael C. Jensen & Ralph J. Dandrea & Gregory J. Gordon & Carl T. Bergstrom, 2013. "Author‐level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(4), pages 787-801, April.
    10. Karki, M. M. S., 1997. "Patent citation analysis: A policy analysis tool," World Patent Information, Elsevier, vol. 19(4), pages 269-272, December.
    11. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    12. Mariani, Manuel Sebastian & Medo, Matúš & Zhang, Yi-Cheng, 2016. "Identification of milestone papers through time-balanced network centrality," Journal of Informetrics, Elsevier, vol. 10(4), pages 1207-1223.
    13. 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.
    14. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    15. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    16. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    17. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    18. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    19. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    20. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    21. Miguelez, Ernest, 2019. "Collaborative patents and the mobility of knowledge workers," Technovation, Elsevier, vol. 86, pages 62-74.
    22. Hsieh, Chih-Hung, 2013. "Patent value assessment and commercialization strategy," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 307-319.
    23. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.
    24. Fen Zhao & Yi Zhang & Jianguo Lu & Ofer Shai, 2019. "Measuring academic influence using heterogeneous author-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1119-1140, March.
    25. Lin, Jia & Wu, Ho-Mou & Wu, Howei, 2021. "Could government lead the way? Evaluation of China's patent subsidy policy on patent quality," China Economic Review, Elsevier, vol. 69(C).
    26. Jungwon Yoon, 2015. "The evolution of South Korea’s innovation system: moving towards the triple helix model?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 265-293, July.
    27. Anne‐Wil Harzing & Ron van der Wal, 2009. "A Google Scholar h‐index for journals: An alternative metric to measure journal impact in economics and business," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 41-46, January.
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    2. Xipeng Liu & Xinmiao Li, 2024. "Unbiased evaluation of ranking algorithms applied to the Chinese green patents citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 2999-3021, June.

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