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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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    1. Ching-Yan Wu, 2014. "Comparisons of technological innovation capabilities in the solar photovoltaic industries of Taiwan, China, and Korea," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 429-446, January.
    2. Ejermo, Olof & Karlsson, Charlie, 2006. "Interregional inventor networks as studied by patent coinventorships," Research Policy, Elsevier, vol. 35(3), pages 412-430, April.
    3. Sungchul Choi & Hyunseok Park, 2016. "Investigation of Strategic Changes Using Patent Co-Inventor Network Analysis: The Case of Samsung Electronics," Sustainability, MDPI, vol. 8(12), pages 1-13, December.
    4. D. F. Westerheijden, 1999. "Innovation indicators in science and technology evaluation: Comments from a higher education point of view," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(3), pages 445-453, July.
    5. Gulbrandsen, Magnus & Smeby, Jens-Christian, 2005. "Industry funding and university professors' research performance," Research Policy, Elsevier, vol. 34(6), pages 932-950, August.
    6. Sercan Ozcan & Nazrul Islam, 2017. "Patent information retrieval: approaching a method and analysing nanotechnology patent collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 941-970, May.
    7. John Panaretos & Chrisovaladis Malesios, 2009. "Assessing scientific research performance and impact with single indices," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 635-670, December.
    8. Panagopoulos, George & Tsatsaronis, George & Varlamis, Iraklis, 2017. "Detecting rising stars in dynamic collaborative networks," Journal of Informetrics, Elsevier, vol. 11(1), pages 198-222.
    9. Hu, Albert G. Z. & Jaffe, Adam B., 2003. "Patent citations and international knowledge flow: the cases of Korea and Taiwan," International Journal of Industrial Organization, Elsevier, vol. 21(6), pages 849-880, June.
    10. Alan L. Porter & Alex S. Cohen & J. David Roessner & Marty Perreault, 2007. "Measuring researcher interdisciplinarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(1), pages 117-147, July.
    11. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    12. Breschi, Stefano & Catalini, Christian, 2010. "Tracing the links between science and technology: An exploratory analysis of scientists' and inventors' networks," Research Policy, Elsevier, vol. 39(1), pages 14-26, February.
    13. Olav Sorenson & Jan W. Rivkin & Lee Fleming, 2010. "Complexity, Networks and Knowledge Flows," Chapters, in: Ron Boschma & Ron Martin (ed.), The Handbook of Evolutionary Economic Geography, chapter 15, Edward Elgar Publishing.
    14. Frank van der Wouden & David L. Rigby, 2019. "Co‐inventor networks and knowledge production in specialized and diversified cities," Papers in Regional Science, Wiley Blackwell, vol. 98(4), pages 1833-1853, August.
    15. Jasjit Singh, 2005. "Collaborative Networks as Determinants of Knowledge Diffusion Patterns," Management Science, INFORMS, vol. 51(5), pages 756-770, May.
    16. Hu, Mei-Chih, 2012. "Technological innovation capabilities in the thin film transistor-liquid crystal display industries of Japan, Korea, and Taiwan," Research Policy, Elsevier, vol. 41(3), pages 541-555.
    17. Tibor Braun & Wolfgang Glänzel & András Schubert, 2006. "A Hirsch-type index for journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 169-173, October.
    18. Ali Daud & Muhammad Ahmad & M. S. I. Malik & Dunren Che, 2015. "Using machine learning techniques for rising star prediction in co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1687-1711, February.
    19. Gabjin Oh & Ho-Yong Kim & Ayoung Park, 2017. "Analysis of technological innovation based on citation information," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1065-1091, May.
    20. Luciano Kay & Nils Newman & Jan Youtie & Alan L. Porter & Ismael Rafols, 2014. "Patent overlay mapping: Visualizing technological distance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2432-2443, December.
    21. Dornbusch, Friedrich & Neuhäusler, Peter, 2015. "Composition of inventor teams and technological progress – The role of collaboration between academia and industry," Research Policy, Elsevier, vol. 44(7), pages 1360-1375.
    22. Maria Bordons & M. T. Fernández & Isabel Gómez, 2002. "Advantages and limitations in the use of impact factor measures for the assessment of research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(2), pages 195-206, February.
    23. Jiancheng Guan & Kairui Zuo, 2014. "A cross-country comparison of innovation efficiency," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 541-575, August.
    24. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
    25. Mavis Chen & Carol Lin & Hsing-Er Lin & Edward McDonough, 2012. "Does transformational leadership facilitate technological innovation? The moderating roles of innovative culture and incentive compensation," Asia Pacific Journal of Management, Springer, vol. 29(2), pages 239-264, June.
    26. Ruslan Lukach & Joseph Plasmans, 2002. "Measuring Knowledge Spillovers Using Patent Citations: Evidence from the Belgian Firm's Data," CESifo Working Paper Series 754, CESifo.
    27. Giovanni Peri, 2005. "Determinants of Knowledge Flows and Their Effect on Innovation," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 308-322, May.
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    Cited by:

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    2. 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).
    3. Ali Daud & Min Song & Malik Khizar Hayat & Tehmina Amjad & Rabeeh Ayaz Abbasi & Hassan Dawood & Anwar Ghani, 2020. "Finding rising stars in bibliometric networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 633-661, July.
    4. 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.
    5. 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).
    6. 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.
    7. Aftab Nawaz & MSI Malik, 2022. "Rising stars prediction in reviewer network," Electronic Commerce Research, Springer, vol. 22(1), pages 53-75, March.
    8. 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).
    9. 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.
    10. 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.
    11. 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.

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