Skip to main content
SpringerLink
Log in

Unbiased evaluation of ranking algorithms applied to the Chinese green patents citation network

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

As a phased achievement of technological innovation, patent analysis holds extraordinary research significance. By constructing patent citation networks, scholars have proposed various centrality algorithms (such as citation count, PageRank, LeaderRank, etc.) for evaluating the quality and influence of patents. However, these centrality algorithms suffer from age bias, which means these algorithms are more inclined to obtain higher rankings for older patents, thus losing fairness to younger patents. Additionally, the selection of algorithm performance evaluation indicators is crucial. If the indicators are not chosen appropriately, the results may be affected. Therefore, based on the background of Chinese green patents, this paper develops an unbiased evaluation ranking algorithm to identify significant Chinese green patents earlier. The results demonstrate that the combination of the rescaled method and the AttriRank algorithm can effectively obtain the importance of patents, and provide a systematic and reasonable evaluation method for measuring patent value.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. http://epub.cnipa.gov.cn/.

  2. https://www.wipo.int/classifications/ipc/green-inventory/home.

  3. http://www.google.com/patents.

  4. https://www.cnipa.gov.cn/col/col41/index.html

References

  • Beltz, H., Rutledge, T., Wadhwa, R. R., Bruck, P., & Érdi, P. (2019). Ranking algorithms: Application for patent citation network. Information Quality in Information Fusion and Decision Making. https://doi.org/10.1007/978-3-030-03643-0_21

    Article  Google Scholar 

  • Bornmann, L., & Williams, R. (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, 11(3), 788–799.

    Article  Google Scholar 

  • Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 1–14.

    Article  Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

    Article  Google Scholar 

  • Chen, D., Lü, L., Shang, M. S., Zhang, Y. C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica a: Statistical Mechanics and Its Applications, 391(4), 1777–1787.

    Article  Google Scholar 

  • Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific gems with Google’s PageRank algorithm. Journal of Informetrics, 1(1), 8–15.

    Article  Google Scholar 

  • Dunaiski, M., Geldenhuys, J., & Visser, W. (2018). On the interplay between normalization, bias, and performance of paper impact metrics. Journal of Informetrics, 13(1), 270–290.

    Article  Google Scholar 

  • Dunaiski, M., Visser, W., & Geldenhuys, J. (2016). Evaluating paper and author ranking algorithms using impact and contribution awards. Journal of Informetrics, 10(2), 392–407.

    Article  Google Scholar 

  • Egghe, L. (2006). Theory and practice of the G-index. Scientometrics, 69(1), 131–152.

    Article  Google Scholar 

  • Érdi, P., Makovi, K., Somogyvári, Z., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1), 225–242.

    Article  Google Scholar 

  • Fiala, D. (2012). Time-aware PageRank for bibliographic networks. Journal of Informetrics, 6(3), 370–388.

    Article  Google Scholar 

  • Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., & Barabási, A. L. (2018). Science of science. Science. https://doi.org/10.1126/science.aao0185

    Article  Google Scholar 

  • Freitas, J., Barbastefano, R., & Carvalho, D. (2018). Identifying influential patents in citation networks using enhanced VoteRank centrality. arXiv preprint: 1811.01638.

  • Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.

    Article  Google Scholar 

  • Hsieh, H. C. (2013). Patent value assessment and commercialization strategy. Technological Forecasting & Social Change, 80(2), 307–319.

    Article  Google Scholar 

  • Hsu, C. C., Lai, Y. A., Chen, W. H., Feng, M. H., & Lin, S. D. (2017). Unsupervised ranking using graph structures and node attributes. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3018661.3018668

    Article  Google Scholar 

  • Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation and growth. The Quarterly Journal of Economics, 132(2), 665–712.

    Article  Google Scholar 

  • Lee, C., Cho, Y., Seol, H., & Park, Y. (2012). A stochastic patent citation analysis approach to assessing future technological impacts. Technological Forecasting & Social Change, 79(1), 16–29.

    Article  Google Scholar 

  • Lee, W. S., Han, E. J., & Sohn, J. D. (2015). Predicting the pattern of technology convergence using bigdata technology on large-scale triadic patents. Technological Forecasting & Social Change, 100, 317–329.

    Article  Google Scholar 

  • Li, Z., Ren, T., Ma, X., Liu, S., Zhang, Y., & Zhou, T. (2019). Identifying influential spreaders by gravity model. Scientific Reports, 9(1), 1–7.

    Google Scholar 

  • Lin, J., Wu, H. M., & Wu, H. (2021). Could government lead the way? Evaluation of China’s patent subsidy policy on patent quality. China Economic Review, 69, 101663.

    Article  Google Scholar 

  • Liu, X., & Li, X. (2022). Early identification of significant patents using heterogeneous applicant-citation networks based on the Chinese green patent data. Sustainability, 14(21), 13870.

    Article  Google Scholar 

  • Lü, L., Zhang, Y. C., Yeung, C. H., & Zhou, T. (2011). Leaders in social networks, the delicious case. PLoS ONE, 6(6), e201202.

    Article  Google Scholar 

  • Luo, D., Gong, C., Hu, R., Duan, L., & Ma, S. (2016). Ensemble enabled weighted PageRank. arXiv preprint: 1604.05462.

  • Mariani, M. S., & Lü, L. (2020). Network-based ranking in social systems: Three challenges. Journal of Physics: Complexity, 1(1), 011001.

    Google Scholar 

  • Mariani, M. S., Medo, M., & Lafond, F. (2019). Early identification of important patents: Design and validation of citation network metrics. Technological Forecasting & Social Change, 146, 644–654.

    Article  Google Scholar 

  • Mariani, M. S., Medo, M., & Zhang, Y. C. (2015). Ranking nodes in growing networks: When PageRank fails. Scientific Reports, 5(1), 16181.

    Article  Google Scholar 

  • Mariani, M. S., Medo, M., & Zhang, Y. C. (2016). Identification of milestone papers through time-balanced network centrality. Journal of Informetrics, 10(4), 1207–1223.

    Article  Google Scholar 

  • Mezzanotti, F. (2021). Roadblock to innovation: The role of patent litigation in corporate R&D. Management Science, 67(12), 7362–7390.

    Article  Google Scholar 

  • Namtirtha, A., Dutta, A., Dutta, B., Sundararajan, A., & Simmhan, Y. (2021). Best influential spreaders identification using network global structural properties. Scientific Reports, 11(1), 1–15.

    Article  Google Scholar 

  • Newman, M. (2010). Networks: An introduction. Oxford University Press.

    Book  Google Scholar 

  • Poege, F., Harhoff, D., Gaessler, F., & Baruffaldi, S. (2019). Science quality and the value of inventions. Science Advances. https://doi.org/10.1126/sciadv.aay7323

    Article  Google Scholar 

  • Radicchi, F., Weissman, A., & Bollen, J. (2017). Quantifying perceived impact of scientific publications. Journal of Informetrics, 11(3), 704–712.

    Article  Google Scholar 

  • Ren Z., Mariani M. S., Zhang Y.C., Medo M. (2017). A time-respecting null model to explore the properties of growing networks, arXiv: 1703.07656.

  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science. https://doi.org/10.1126/science.aaf5239

    Article  Google Scholar 

  • Son, J., & Kim, S. (2018). Academic paper recommender system using multilevel simultaneous citation networks. Decision Support Systems, 105, 24–33.

    Article  Google Scholar 

  • Strumsky, D., & Lobo, J. (2015). Identifying the sources of technological novelty in the process of invention. Research Policy, 44(8), 1445–1461.

    Article  Google Scholar 

  • Vaccario, G., Medo, M., Wider, N., & Mariani, M. S. (2017). Quantifying and suppressing ranking bias in a large citation network. Journal of Informetrics, 11(3), 766–782.

    Article  Google Scholar 

  • Walker, D., Xie, H., Yan, K. K., & Maslov, S. (2007). Ranking scientific publications using a model of network traffic. Journal of Statistical Mechanics: Theory and Experiment, 06, P06010.

    Google Scholar 

  • Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391.

    Article  Google Scholar 

  • Wang, Y., Zeng, A., Fan, Y., & Di, Z. (2019). Ranking scientific publications considering the aging characteristics of citations. Scientometrics, 120, 155–166.

    Article  Google Scholar 

  • Wasserman, M., Zeng, X., & Amaral, L. (2015). Cross-evaluation of metrics to estimate the significance of creative works. Preceedings of the National Academy of Sciences, 112(5), 1281–1286.

    Article  MathSciNet  Google Scholar 

  • Xu, S., Mariani, M. S., Lü, L., & Medo, M. (2020). Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data. Journal of Informetrics, 14(1), 101005.

    Article  Google Scholar 

  • Zhang, J., Chen, D., Dong, Q., & Zhao, Z. (2016). Identifying a set of influential spreaders in complex networks. Scientific Reports, 6(1), 1–10.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, Shanghai University, Shanghai, 200444, China

    Xipeng Liu

  2. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Xinmiao Li

Authors
  1. Xipeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinmiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xipeng Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Li, X. Unbiased evaluation of ranking algorithms applied to the Chinese green patents citation network. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05023-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11192-024-05023-1

Keywords

Search

Navigation