IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v121y2019i1d10.1007_s11192-019-03194-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03194-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-019-03194-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. 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.
    4. Aftab Nawaz & MSI Malik, 2022. "Rising stars prediction in reviewer network," Electronic Commerce Research, Springer, vol. 22(1), pages 53-75, March.
    5. 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.
    6. 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.
    7. 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).
    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. 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.
    10. 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.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niccolò Innocenti & Francesco Capone & Luciana Lazzeretti & Sergio Petralia, 2022. "The role of inventors’ networks and variety for breakthrough inventions," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 37-57, February.
    2. Tubiana, Matteo & Miguelez, Ernest & Moreno, Rosina, 2022. "In knowledge we trust: Learning-by-interacting and the productivity of inventors," Research Policy, Elsevier, vol. 51(1).
    3. Xia Gao & Jiancheng Guan & Ronald Rousseau, 2011. "Mapping collaborative knowledge production in China using patent co-inventorships," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 343-362, August.
    4. Park, Jongyong & Lee, Hakyeon & Park, Yongtae, 2009. "Disembodied knowledge flows among industrial clusters: A patent analysis of the Korean manufacturing sector," Technology in Society, Elsevier, vol. 31(1), pages 73-84.
    5. Gao, Xue & Zhang, Yi, 2022. "What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Autant-Bernard, Corinne & Fadairo, Muriel & Massard, Nadine, 2013. "Knowledge diffusion and innovation policies within the European regions: Challenges based on recent empirical evidence," Research Policy, Elsevier, vol. 42(1), pages 196-210.
    7. Fabio Montobbio & Annalisa Primi & Valerio Sterzi, 2015. "IPRs and International Knowledge Flows: Evidence from Six Large Emerging Countries," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(2), pages 187-204, April.
    8. Lorenzo Cassi & Anne Plunket, 2015. "Research Collaboration in Co-inventor Networks: Combining Closure, Bridging and Proximities," Regional Studies, Taylor & Francis Journals, vol. 49(6), pages 936-954, June.
    9. Mei-Chih Hu & Ching-Yan Wu & Jung Hoon Lee & Yun-Chu Lu, 2014. "The influence of knowledge source and ambidexterity in the thin film transistor and liquid crystal display industry: evidence from Japan, Korea, and Taiwan," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 233-260, May.
    10. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    11. Holger Graf, 2013. "Inventor Networks in Emerging Key Technologies: Information Technology vs. Semiconductors," Economic Complexity and Evolution, in: Guido Buenstorf & Uwe Cantner & Horst Hanusch & Michael Hutter & Hans-Walter Lorenz & Fritz Rahmeyer (ed.), The Two Sides of Innovation, edition 127, pages 55-76, Springer.
    12. Lorenzo Cassi & Anne Plunket, 2010. "The determinants of co-inventor tie formation: proximity and network dynamics," Papers in Evolutionary Economic Geography (PEEG) 1015, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Nov 2010.
    13. Ron Boschma & Pierre-Alexandre Balland & Dieter Kogler, 2011. "A relational approach to knowledge spillovers in biotech. Network structures as drivers of inter-organizational citation patterns," Papers in Evolutionary Economic Geography (PEEG) 1120, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2011.
    14. 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.
    15. Pierre-Alexandre Balland & Ron Boschma & Julien Ravet, 2019. "Network dynamics in collaborative research in the EU, 2003–2017," European Planning Studies, Taylor & Francis Journals, vol. 27(9), pages 1811-1837, September.
    16. van der Wouden, Frank & Youn, Hyejin, 2023. "The impact of geographical distance on learning through collaboration," Research Policy, Elsevier, vol. 52(2).
    17. Araki, Michael E. & Bennett, Daniel L. & Wagner, Gary A., 2024. "Regional innovation networks & high-growth entrepreneurship," Research Policy, Elsevier, vol. 53(1).
    18. Goossen, Martin C. & Paruchuri, Srikanth, 2022. "Measurement errors and estimation biases with incomplete social networks: replication studies on intra-firm inventor network analysis," Research Policy, Elsevier, vol. 51(1).
    19. Stephen Carley & Alan L. Porter, 2012. "A forward diversity index," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 407-427, February.
    20. Deming Lin & Tianhui Gong & Wenbin Liu & Martin Meyer, 2020. "An entropy-based measure for the evolution of h index research," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2283-2298, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:121:y:2019:i:1:d:10.1007_s11192-019-03194-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.