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Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction

Author

Listed:
  • Yan Qi

    (Chinese Academy of Medical Sciences/Peking Union Medical College (CAMS&PUMC))

  • Xin Zhang

    (Chinese Academy of Sciences)

  • Zhengyin Hu

    (Chinese Academy of Sciences)

  • Bin Xiang

    (Chinese Academy of Sciences)

  • Ran Zhang

    (Chinese Academy of Medical Sciences/Peking Union Medical College (CAMS&PUMC))

  • Shu Fang

    (Chinese Academy of Sciences)

Abstract

Selecting the right collaboration partner is one of the most important contributors to success in collaborative innovation. Accordingly, numerous methods for selecting an appropriate partner have been developed to guide would-be collaborators in their search. Most rely on bibliographic information, which may be easier for that data is readily available and relatively normalized. However, with the benefit of today’s text mining and fusion techniques, it is possible to mine the content of papers and patents so as to result in far more nuanced and advantageous choices. In this article, we explore how to select partners for collaborative innovation by combining the characteristics of the authors of paper and patent documents as well as their content. Drawing on existing research, we developed a systematic framework that relies on topic analysis and link prediction. With a corpus of papers and patents assembled, the framework extracts correlated scientific and technological topics followed by a list of author institutions and a list of patentees. These organisations are parsed and evaluated using two indicators of innovation—capability and openness—to result in two separate ranked lists. Two integrated collaboration networks that include both author institutions and patentees are then built, and a link prediction method identifies missing links with a high likelihood of fruitful cooperation. A case study on hepatitis C virus research shows that the ranking procedure and the link prediction method can be used either together or separately to effectively identify collaborative innovation partners. Our results provide significant quantitative evidence for policymakers who are looking to foster cooperation between research institutions and/or high-tech enterprises. Our research may also serve as the basis for further in-depth research on collaborative innovation, R&D cooperation, and link prediction theories and methods.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:9:d:10.1007_s11192-022-04306-9
    DOI: 10.1007/s11192-022-04306-9
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    3. Xiuxiu Li & Mingyang Wang & Xu Liu, 2024. "Predicting collaborative relationship among scholars by integrating scholars’ content-based and structure-based features," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3225-3244, June.
    4. Csizmadia, Péter & Kosztyán, Zsolt Tibor & Fehérvölgyi, Beáta & Hausz, Frigyes, 2024. "Intézményi koncentráció és az innovációs hálózatok vizsgálata [Institutional concentration and innovation networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 1351-1380.

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