IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v101y2014i2d10.1007_s11192-014-1304-9.html
   My bibliography  Save this article

Mapping the evolving patterns of patent assignees’ collaboration networks and identifying the collaboration potential

Author

Listed:
  • Yunwei Chen

    (Chengdu Library of the Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shu Fang

    (Chengdu Library of the Chinese Academy of Sciences)

Abstract

The purpose of this article is to map the evolving patterns of patent assignees’ collaboration networks and build a latent collaboration index (LCI) model for evaluating the collaboration probability among assignees. The demonstration process was carried on the field of industrial biotechnology (IB) from 2000 to 2010. The results show that the number of assignees in the field of IB grew steadily, while the number of patents decreased slowly year by year after it reached peak in 2002 and 2003. Densification and growth analysis, average degree, density and components analysis showed that the collaboration networks tended to density. Especially the diameter analysis indicated that the IB field had come into a mature mode after finishing the topological transition occurred in about 2002 or 2003. The nodes had degree k followed a power law distribution, which implied a preferential linking feature of the network evolving and thus provided a foundation for link prediction from the aspect of network evolving. Basing on this, two network-related factors had been brought into the LCI model, which were degree and network distance. Their values were positive and negative for link prediction respectively. In addition, types of assignees, geographical distances and topics similarities had also been added into the LCI model. Different types of assignees had also different probabilities to be linked, such as corporations had been collaborated more frequently, while universities ranked lowest based on collaborations. Assignees from the same countries seemed to be likely to collaborate to each other. It have to been noted that the LCI model is flexible that can be adjusted of the factors or their weights according to different subjects, time or data. For instance, the topics similarities between assignees would be removed from the LCI model for link prediction in the field of IB because of the poor inference from topics similarities to collaborations. Actually, many promising pairs of assignees that seemed to have the potential to collaborate to each other according to one or more of these factors have never collaborated. One possible reason might be that collaboration is not popular behaviours among assignees during the process of patent application or maintain. Another reason could be the competitions between assignees. Many a time the promising pairs are competing pairs. Therefore, it was hard to carry out regression analysis basing on those four factors to get usable coefficients set of the four factors. The LCI model could only be used to make qualitative analysis on collaboration potential when it was revised.

Suggested Citation

  • Yunwei Chen & Shu Fang, 2014. "Mapping the evolving patterns of patent assignees’ collaboration networks and identifying the collaboration potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1215-1231, November.
  • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1304-9
    DOI: 10.1007/s11192-014-1304-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1304-9
    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/s11192-014-1304-9?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. Yang, Yun Yun & Akers, Lucy & Yang, Cynthia Barcelon & Klose, Thomas & Pavlek, Shelley, 2010. "Enhancing patent landscape analysis with visualization output," World Patent Information, Elsevier, vol. 32(3), pages 203-220, September.
    2. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    3. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
    4. Dou, Henri & Bai, Ying, 2007. "A rapid analysis of Avian Influenza patents in the Esp@cenet® database - R&D strategies and country comparisons," World Patent Information, Elsevier, vol. 29(1), pages 26-32, March.
    5. Hanaki, Nobuyuki & Nakajima, Ryo & Ogura, Yoshiaki, 2010. "The dynamics of R&D network in the IT industry," Research Policy, Elsevier, vol. 39(3), pages 386-399, April.
    6. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    7. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    8. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    9. Gress, Bernard, 2010. "Properties of the USPTO patent citation network: 1963-2002," World Patent Information, Elsevier, vol. 32(1), pages 3-21, March.
    10. Shiu-Wan Hung & An-Pang Wang, 2010. "Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 121-134, January.
    11. Han, Yoo-Jin & Park, Yongtae, 2006. "Patent network analysis of inter-industrial knowledge flows: The case of Korea between traditional and emerging industries," World Patent Information, Elsevier, vol. 28(3), pages 235-247, September.
    Full references (including those not matched with items on IDEAS)

    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. Choe, Hochull & Lee, Duk Hee & Seo, Il Won & Kim, Hee Dae, 2013. "Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 492-505.
    2. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    3. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 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, Elsevier, vol. 115(C), pages 210-220.
    4. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    5. 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.
    6. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    7. Sung-Seok Ko & Namuk Ko & Doyeon Kim & Hyunseok Park & Janghyeok Yoon, 2014. "Analyzing technology impact networks for R&D planning using patents: combined application of network approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 917-936, October.
    8. Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    9. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    10. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    11. 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.
    12. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    13. Lai, Kuei-Kuei & Chen, Yu-Long & Kumar, Vimal & Daim, Tugrul & Verma, Pratima & Kao, Fang-Chen & Liu, Ruirong, 2023. "Mapping technological trajectories and exploring knowledge sources: A case study of E-payment technologies," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    14. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    15. Choe, Hochull & Lee, Duk Hee & Kim, Hee Dae & Seo, Il Won, 2016. "Structural properties and inter-organizational knowledge flows of patent citation network: The case of organic solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 361-370.
    16. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    17. Cristiano Antonelli & Francesco Crespi & Christian A. Mongeau Ospina & Giuseppe Scellato, 2017. "Knowledge composition, Jacobs externalities and innovation performance in European regions," Regional Studies, Taylor & Francis Journals, vol. 51(11), pages 1708-1720, November.
    18. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    19. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.
    20. Jyun-Cheng Wang & Cheng-hsin Chiang & Shu-Wei Lin, 2010. "Network structure of innovation: can brokerage or closure predict patent quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 735-748, September.

    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:101:y:2014:i:2:d:10.1007_s11192-014-1304-9. 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.