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Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches

Author

Listed:
  • Wei Chen

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Hui Qu

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Kuo Chi

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

Abstract

To enhance competitiveness and protect interest, an increasing number of organizations cooperate on patent applications. Partner selection has attracted much more attention because it directly affects the success of patent cooperation. By collecting some cooperative patents applied for by different categories of organizations in China from 2007 to 2015, an interorganizational patent cooperation network was built for this paper. After analyzing certain basic properties of the network, it was found that the network possessed some typical characteristics of social networks. Moreover, the network could be divided into communities, and three communities were selected to analyze as representative. Furthermore, to explore the partner selection in the patent cooperation network, eight link prediction approaches commonly used in social networks were introduced to run on another interorganizational patent cooperation network built by the patents applied for in 2016. The precision metric results of the eight link prediction approaches show that they are effective in partnership prediction; in particular, the Common Neighbors (CN) index can be effectively applied to the selection of unfamiliar partners for organizations in patent cooperation. Moreover, this paper also verified the trust transitivity based not only on historical cooperation but also on geographical location, and the complementarity of capabilities still plays an important role in partner selection for organizations.

Suggested Citation

  • Wei Chen & Hui Qu & Kuo Chi, 2021. "Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:1003-:d:483288
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    3. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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    5. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    6. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    7. Yuewei Wang & Hang Chen & Xinyang Wu, 2021. "Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow," Sustainability, MDPI, vol. 13(21), pages 1-16, October.

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