Skip to main content
Log in

Improving the identification effect of technical trajectory by adding ghost edges in the patent citation network

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This paper proposes a method to improve the identification effect of technical Trajectory by adding ghost edges in the patent citation network, which includes calculating patent technology similarity, constructing ghost edge candidate set, adding the ghost edges by evaluating the utility measures, and using main path analysis to identify four technical trajectories. Taking US e-commerce data technology as an example, we find the following three points. (1) Adding a small amount of ghost edges in the patent citation network helps to increase the accuracy of technical trajectory identification, but adding a large number of ghost edges may cause destructive effects on the network structure and lead to identification bias. The experience value of this case is at most 10%. (2) Different construction methods of ghost edge candidate sets will have an important impact on the result of improving the trajectory recognition. No matter which candidate set is used, there is no deviation in the primary technical trajectory identification. However, there are differences in the subsequent technical trajectory identification. (3) The addition of the ghost edges further improves the network characteristics, especially the technical trajectory differences in subsequent locations which are identified.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Dosi, G. (1982). Technological paradigms and technological trajectories. Research Policy, 11(3), 147–162. https://doi.org/10.1016/0048-7333(82)90016-6

    Article  Google Scholar 

  2. Malhotra, A., Zhang, H., Beuse, M., & Schmidt, T. (2021). How do new use environments influence a technology’s knowledge trajectory? A patent citation network analysis of lithium-ion battery technology. Research Policy, 50(9), 104318. https://doi.org/10.1016/j.respol.2021.104318

    Article  Google Scholar 

  3. Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504–1511. https://doi.org/10.1287/mnsc.35.12.1504

    Article  Google Scholar 

  4. Sharma, P., & Tripathi, R. C. (2017). Patent citation: A technique for measuring the knowledge flow of information and Innovation. World Patent Information, 51, 31–42. https://doi.org/10.1016/j.wpi.2017.11.002

    Article  Google Scholar 

  5. Alessandri, E. (2023). Identifying technological trajectories in the mining sector using patent citation networks. Resources Policy, 80, 103130. https://doi.org/10.1016/j.resourpol.2022.103130

    Article  Google Scholar 

  6. Malerba, F., & Orsenigo, L. (1993). Technological regimes and firm bebavior. Industrial and Corporate Change, 2(1), 45–71. https://doi.org/10.1093/icc/2.1.45

    Article  Google Scholar 

  7. Huang, Y., Li, R., Zou, F., Jiang, L., Porter, A. L., & Zhang, L. (2022). Technology life cycle analysis: From the dynamic perspective of patent citation networks. Technological Forecasting and Social Change, 181, 121760. https://doi.org/10.1016/j.techfore.2022.121760

    Article  Google Scholar 

  8. Karki, M. M. S. (1997). Patent citation analysis: A policy analysis tool. World Patent Information, 19(4), 269–272. https://doi.org/10.1016/s0172-2190(97)00033-1

    Article  Google Scholar 

  9. Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63. https://doi.org/10.1016/0378-8733(89)90017-8

    Article  Google Scholar 

  10. Verspagen, B. (2007). Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Advances in Complex Systems, 10(01), 93–115. https://doi.org/10.26481/umamer.2005020

    Article  Google Scholar 

  11. Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks. An application to data communication standards. Economics of Innovation and New Technology, 18(4), 311–336. https://doi.org/10.1080/10438590801969073

    Article  Google Scholar 

  12. Batagelj, V. (2003). Efficient algorithms for citation network analysis. Computer Science. https://doi.org/10.48550/arXiv.cs/0309023

  13. Martinelli, A. (2012). An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry. Research Policy, 41(2), 414–429. https://doi.org/10.1016/j.respol.2011.10.012

    Article  Google Scholar 

  14. Angelou, K., Maragakis, M., Kosmidis, K., & Argyrakis, P. (2020). A hybrid model for the patent citation network structure. Physica A: Statistical Mechanics and Its Applications, 541, 123363. https://doi.org/10.1016/j.physa.2019.123363

    Article  Google Scholar 

  15. Chen, L. (2017). DO patent citations indicate knowledge linkage? The evidence from text similarities between patents and their citations. Journal of Informetrics, 11(1), 63–79. https://doi.org/10.1016/j.joi.2016.04.018

    Article  Google Scholar 

  16. Cotropia, C. A., Lemley, M. A., & Sampat, B. (2013). Do applicant patent citations matter? Research Policy, 42(4), 844–854. https://doi.org/10.1016/j.respol.2013.01.003

    Article  Google Scholar 

  17. Lampe, R. (2010). Strategic citation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.984123

    Article  Google Scholar 

  18. Yoon, B., & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. The Journal of High Technology Management Research, 15(1), 37–50. https://doi.org/10.1016/j.hitech.2003.09.003

    Article  Google Scholar 

  19. Niemann, H., Moehrle, M. G., & Frischkorn, J. (2017). Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, Method and test application. Technological Forecasting and Social Change, 115, 210–220. https://doi.org/10.1016/j.techfore.2016.10.004

    Article  Google Scholar 

  20. Song, K., Kim, K. S., & Lee, S. (2017). Discovering new technology opportunities based on patents: Text-mining and F-term analysis. Technovation, 60–61, 1–14. https://doi.org/10.1016/j.technovation.2017.03.001

    Article  Google Scholar 

  21. Choi, J., & Hwang, Y.-S. (2014). Patent keyword network analysis for improving technology development efficiency. Technological Forecasting and Social Change, 83, 170–182. https://doi.org/10.1016/j.techfore.2013.07.004

    Article  Google Scholar 

  22. Madani, F., & Weber, C. (2016). The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Patent Information, 46, 32–48. https://doi.org/10.1016/j.wpi.2016.05.008

    Article  Google Scholar 

  23. No, H. J., An, Y., & Park, Y. (2015). A structured approach to explore knowledge flows through technology-based business methods by integrating patent citation analysis and text mining. Technological Forecasting and Social Change, 97, 181–192. https://doi.org/10.1016/j.techfore.2014.04.007

    Article  Google Scholar 

  24. Hou, J., Tang, S., Zhang, Y., & Song, H. (2023). Does prior knowledge affect patent technology diffusion? A semantic-based patent citation contribution analysis. Journal of Informetrics, 17(2), 101393. https://doi.org/10.1016/j.joi.2023.101393

    Article  Google Scholar 

  25. Bryan, K. A., Ozcan, Y., & Sampat, B. (2020). In-text patent citations: A user’s guide. Research Policy, 49(4), 103946. https://doi.org/10.1016/j.respol.2020.103946

    Article  Google Scholar 

  26. Rodriguez, A., Kim, B., Lee, J.-M., Coh, B.-Y., & Jeong, M. K. (2015). Graph kernel based measure for evaluating the influence of patents in a patent citation network. Expert Systems with Applications, 42(3), 1479–1486. https://doi.org/10.1016/j.eswa.2014.08.051

    Article  Google Scholar 

  27. Higham, K., Contisciani, M., & De Bacco, C. (2022). Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships. Technological Forecasting and Social Change, 179, 121628. https://doi.org/10.1016/j.techfore.2022.121628

    Article  Google Scholar 

  28. Qiu, Z., & Wang, Z. (2023). Technological origination and evolution analysis by combining patent claims and citations: A case of surgical robot domain. Advanced Engineering Informatics, 58, 102145. https://doi.org/10.1016/j.aei.2023.102145

    Article  Google Scholar 

  29. Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42(9), 4348–4360. https://doi.org/10.1016/j.eswa.2015.01.050

    Article  Google Scholar 

  30. Yoon, J., & Kim, K. (2012). An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells. Expert Systems with Applications, 39(9), 7709–7717. https://doi.org/10.1016/j.eswa.2012.01.035

    Article  Google Scholar 

  31. Dewulf, S. (2011). Directed variation of properties for new or improved function product DNA—a base for connect and develop. Procedia Engineering, 9, 646–652. https://doi.org/10.1016/j.proeng.2011.03.150

    Article  Google Scholar 

  32. Jatnika, D., Bijaksana, M. A., & Suryani, A. A. (2019). Word2Vec model analysis for semantic similarities in English words. Procedia Computer Science, 157, 160–167. https://doi.org/10.1016/j.procs.2019.08.153

    Article  Google Scholar 

  33. Alami, N., Meknassi, M., & En-nahnahi, N. (2019). Enhancing unsupervised neural networks based text summarization with word embedding and ensemble learning. Expert Systems with Applications, 123, 195–211. https://doi.org/10.1016/j.eswa.2019.01.037

    Article  Google Scholar 

  34. Mikolov, T., Yih, W. T., & Zweig, G. (2013). Linguistic regularities in continuous space word representations. North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics.

  35. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., & Gomez, A. N., et al. (2017).Attention is all you need. arXiv.

  36. Tan, Z., Wang, M., Xie, J., Chen, Y., & Shi, X. (2018). Deep semantic role labeling with self-attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32(1). https://doi.org/10.1609/aaai.v32i1.11928

  37. Iyyer, M., Manjunatha, V., Boyd-Graber, J., & Daumé III, H. (2015). Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). https://doi.org/10.3115/v1/p15-1162

  38. Hermann, K. M., & Blunsom, P. (2013). The Role of Syntax in Vector Space Models of Compositional Semantics. Meeting of the Association for Computational Linguistics.

  39. Papagelis, M., Bonchi, F., & Gionis, A. (2011). Suggesting ghost edges for a smaller world. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. https://doi.org/10.1145/2063576.2063952

  40. Gallagher, B., Tong, H., Eliassi-Rad, T., & Faloutsos, C. (2008). Using ghost edges for classification in sparsely labeled networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/1401890.1401925

  41. Angelou, K., Maragakis, M., & Argyrakis, P. (2019). A structural analysis of the patent citation network by the K-shell decomposition method. Physica A: Statistical Mechanics and Its Applications, 521, 476–483. https://doi.org/10.1016/j.physa.2019.01.063

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the Young and middle-aged Teacher Training Action Discipline (professional) leader training project of Anhui Educational Committee (numberDTR2023092), the Natural Science Research Major Project of Anhui Educational Committee (number2023AH040319), and the Applied Basic Research Project of Wuhu (number 2022jc36).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yulin Liu.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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, Y., Jian, L. Improving the identification effect of technical trajectory by adding ghost edges in the patent citation network. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09830-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10660-024-09830-9

Keywords

Navigation