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An integrated solution for detecting rising technology stars in co-inventor networks

Author

Listed:
  • Lin Zhu

    (Beijing Institute of Technology
    Delft University of Technology)

  • Donghua Zhu

    (Beijing Institute of Technology)

  • Xuefeng Wang

    (Beijing Institute of Technology)

  • Scott W. Cunningham

    (Delft University of Technology)

  • Zhinan Wang

    (Beijing Institute of Technology)

Abstract

Online patent databases are powerful resources for tech mining and social network analysis and, especially, identifying rising technology stars in co-inventor networks. However, it’s difficult to detect them to meet the different needs coming from various demand sides. In this paper, we present an unsupervised solution for identifying rising stars in technological fields by mining patent information. The solution integrates three distinct aspects including technology performance, sociability and innovation caliber to present the profile of inventor, meantime, we design a series of features to reflect multifaceted ‘potential’ of an inventor. All features in the profile can get weights through the Entropy weight method, furthermore, these weights can ultimately act as the instruction for detecting different types of rising technology stars. A K-Means algorithm using clustering validity metrics automatically groups the inventors into clusters according to the strength of each inventor’s profile. In addition, using the nth percentile analysis of each cluster, this paper can infer which cluster with the most potential to become which type of rising technology stars. Through an empirical analysis, we demonstrate various types of rising technology stars: (1) tech-oriented RT Stars: growth of output and impact in recent years, especially in the recent 2 years; active productivity and impact over the last 5 years; (2) social-oriented RT Stars: own an extended co-inventor network and greater potential stemming from those collaborations; (3) innovation-oriented RT Stars: Various technical fields with strong innovation capabilities. (4) All-round RT Stars: show prominent potential in at least two aspects in terms of technical performance, sociability and innovation caliber.

Suggested Citation

  • Lin Zhu & Donghua Zhu & Xuefeng Wang & Scott W. Cunningham & Zhinan Wang, 2019. "An integrated solution for detecting rising technology stars in co-inventor networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 137-172, October.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:1:d:10.1007_s11192-019-03194-w
    DOI: 10.1007/s11192-019-03194-w
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    2. Ekaterina Turkina & Boris Oreshkin, 2021. "The Impact of Co-Inventor Networks on Smart Cleantech Innovation: The Case of Montreal Agglomeration," Sustainability, MDPI, vol. 13(13), pages 1-17, June.
    3. Mariia Shkolnykova, 2021. "Who shapes plant biotechnology in Germany? Joint analysis of the evolution of co-authors’ and co-inventors’ networks," Review of Evolutionary Political Economy, Springer, vol. 2(1), pages 27-54, April.
    4. Wang, Chun-Chieh & Lin, Jia-Tian & Chen, Dar-Zen & Lo, Szu-Chia, 2023. "A New Look at National Diversity of Inventor Teams within Organizations," Journal of Informetrics, Elsevier, vol. 17(1).
    5. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
    6. Aftab Nawaz & MSI Malik, 2022. "Rising stars prediction in reviewer network," Electronic Commerce Research, Springer, vol. 22(1), pages 53-75, March.
    7. Lin Zhu & Junjie Zhang & Scott W. Cunningham, 2022. "Domain expertise extraction for finding rising stars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5475-5495, September.
    8. Yutao Sun & Ying Zhang & Xiaofei Zhang, 2023. "Reconfiguring star inventors with commercialization: a case of the graphene sector," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5411-5440, October.
    9. Fang Han & Sejun Yoon & Nagarajan Raghavan & Hyunseok Park, 2022. "Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics," Sustainability, MDPI, vol. 14(5), pages 1-19, March.
    10. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    11. Chung, Jaemin & Ko, Namuk & Kim, Hyeonsu & Yoon, Janghyeok, 2021. "Inventor profile mining approach for prospective human resource scouting," Journal of Informetrics, Elsevier, vol. 15(1).

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