Skip to main content
SpringerLink
Log in

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

  • Published:
Scientometrics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Alexiev, A. S., Volberda, H. W., & Van den Bosch, F. A. J. (2016). Interorganizational collaboration and firm innovativeness: Unpacking the role of the organizational environment. Journal of Business Research, 69(2), 974–984.

    Article  Google Scholar 

  • Ardito, L., Albino, V., & Messeni Petruzzelli, A. (2019a). The innovative implications of alliance ambidexterity and the moderating role of firm age. IEEE Transactions on Engineering Management, in press.

  • Ardito, L., Galati, F., Messeni Petruzzelli, A., & Corvino, A. (2019b). Improving the financial performance of SMEs. Business Process Management Journal, 26(5), 1041–1054.

    Article  Google Scholar 

  • Ariño, A., Abramov, M., Skorobogatykh, I., Rykounina, I., & Vila, J. (1997). Partner selection and trust building in west European-Russian joint ventures. International Studies of Management and Organization, 27(1), 19–37.

    Article  Google Scholar 

  • Autant, B. C. (2001). Science and knowledge flows: Evidence from the French case. Research Policy, 30(7), 1069–1078.

    Article  Google Scholar 

  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

    Article  Google Scholar 

  • Bassecoulard, E., & Zitt, M. (2004). Patents and publications: The lexical connection. Handbook of quantitative science and technology research: The use of publication and patent statistics in studies of S&T systems (pp. 665–694). Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Becker, W., & Dietz, J. (2004). R&D cooperation and innovation activities of firms—Evidence for the German manufacturing industry. Research Policy, 33(2), 209–223.

    Article  Google Scholar 

  • Bhattacharya, S., Kretschmer, H., & Meyer, M. (2003). Characterizing intellectual spaces between science and technology. Scientometrics, 58(2), 369–390.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    MATH  Google Scholar 

  • Bonaccorsi, A., & Thoma, G. (2007). Institutional complementarity and inventive performance in nano-science and technology. Research Policy, 36(6), 813–831.

    Article  Google Scholar 

  • Brouthers, K. D., Brouthers, L. E., & Wilkinson, T. J. (1995). Strategic alliances: Choose your partners. Long Range Planning, 28(3), 18–25.

    Article  Google Scholar 

  • Cao, X., & Song, Q. (2016). The selection of I-U cooperative partner based on industry’s QFD and improved VIKOR. Science and Technology Management Research, 8, 91–97. In Chinese.

    Google Scholar 

  • Capaldo, A., & Petruzzelli, A. M. (2014). Partner geographic and organizational proximity and the innovative performance of knowledge-creating alliances. European Management Review, 11, 63–84.

    Article  Google Scholar 

  • Carayannis, E. G., Kassicieh, S. K., & Radosevich, R. (2000). Strategic alliances as a source of early-stage seed capital in new technology-based firms. Technovation, 20(11), 603–615.

    Article  Google Scholar 

  • Carpenter, M. P., & Narin, F. (1983). Validation study: Patent citations as indicators of science and foreign dependence. World Patent Information, 5(3), 180–185.

    Article  Google Scholar 

  • Chen, J., & Yang, Y. J. (2012). Theoretical basis and content for collaborative innovation. Studies in Science of Science, 32(2), 161–164. In Chinese.

    Google Scholar 

  • Chen, W., Qu, H., & Chi, K. (2021). Partner selection in China interorganizational patent cooperation network based on link prediction approaches. Sustainability, 13(2), 1003.

    Article  Google Scholar 

  • Chesbrough, H. (2003). Open innovation: How companies actually do it? Harvard Business Review, 81(7), 12–14.

    Google Scholar 

  • Clauss, T., & Kesting, T. (2017). How businesses should govern knowledge-intensive collaborations with universities: An empirical investigation of university professors. Industrial Marketing Management, 62, 185–198.

    Article  Google Scholar 

  • Cloodt, M., Hagedoorn, J., & Van Kranenburg, H. (2006). Mergers and acquisitions: Their effect on the innovative performance of companies in high-tech industries. Research Policy, 35(5), 642–654.

    Article  Google Scholar 

  • Cui, J., Ding, W. Q., Ma, J. H., & Yuan, H. M. (2021). Identification and selection of R&D partners based on technology concentric diversification: Taking Chinese medicine enterprises as an example. Science and Technology Management Research, 4, 133–139. In Chinese.

    Google Scholar 

  • Dacin, M. T., Hitt, M. A., & Levitas, E. (1997). Selecting partners for successful international alliances: Examination of US and Korean firms. Journal of World Business, 32(1), 3–16.

    Article  Google Scholar 

  • De Solla Price, D. J. (1965). Is technology historically independent of science? A study in statistical historiography. Technology and Culture, 6(4), 553–568.

    Article  Google Scholar 

  • Ding, J. D., & Guo, J. (2021). Mining potential author cooperative relationships based on the similarity of content and path. Information Studies: Theory & Application, 44(1), 124-128+123. In Chinese.

    MathSciNet  Google Scholar 

  • Dong, K., Xu, H. Y., Luo, R., Wang, C., & Fang, S. (2018). Review of the research on relationship between science and technology. Journal of the China Society for Scientific and Technical Information, 37(6), 642–652.

    Google Scholar 

  • Doz, Y. (1996). The evolution of cooperation in strategic alliances: Initial conditions or learning processes? Strategic Management Journal, 17(S1), 55–83.

    Article  Google Scholar 

  • Elia, S., Messeni Petruzzelli, A., & Piscitello, L. (2019). The impact of cultural diversity on innovation performance of MNC subsidiaries in strategic alliances. Journal of Business Research, 98, 204–213.

    Article  Google Scholar 

  • Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. R. C. (2000). The future of the university and the university of the future: Evolution of ivory tower to entrepreneurial paradigm. Research Policy, 29(2), 313–330.

    Article  Google Scholar 

  • Friesike, S., Widenmayer, B., Gassmann, O., & Schildhauer, T. (2015). Opening science: Towards an agenda of open science in academia and industry. The Journal of Technology Transfer, 40(4), 581–601.

    Article  Google Scholar 

  • Garfield, E. (1984). Patent citation indexing and the notions of novelty, similarity and relevance. Essays of an Information Scientist, 7(3), 536–542.

    Google Scholar 

  • Garg, K. C. (2001). A study of collaboration in laser science and technology. Scientometrics, 51(2), 415–427.

    Article  Google Scholar 

  • Geum, Y., Lee, S., Yoon, B., & Park, Y. (2013). Identifying and evaluating strategic partners for collaborative R&D: Index-based approach using patents and publications. Technovation, 33(6–7), 211–224.

    Article  Google Scholar 

  • Gnyawali, D. R., & Park, B.-J. (2011). Co-opetition between giants: Collaboration with competitors for technological innovation. Research Policy, 40(5), 650–663.

    Article  Google Scholar 

  • Guns, R., & Rousseau, R. (2014). Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101(2), 1461–1473.

    Article  Google Scholar 

  • Hagedoorn, J. (1993). Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal, 14(5), 371–385.

    Article  Google Scholar 

  • Hagedoorn, J. (2002). Inter-firm R&D partnerships: An overview of major trends and patterns since1960. Research Policy, 31(4), 477–492.

    Article  Google Scholar 

  • Hagedoorn, J., Roijakkers, N., & Kranenburg, H. V. (2008). The formation of subsequent inter-firm R&D partnerships between large pharmaceutical companies and small, entrepreneurial biotechnology firms – how important is inter-organisational trust? International Journal of Technology Management, 44(1–2), 81–92.

    Article  Google Scholar 

  • Han, J., Tang, X., & Yu, A. L. (2021). Research on identification of potential partnership based on link prediction with multilayer networks. Systems Engineering-Theory & Practice, 41(4), 1049–1060. In Chinese.

    Google Scholar 

  • Hitt, M. A., Dacin, M. T., Levitas, E., Arregle, J. L., & Borza, A. (2000). Partner selection in emerging and developed market contexts: Resource-based and organizational learning perspectives. Academy of Management Journal, 43(3), 449–467.

    Google Scholar 

  • Hu, A., Colella, M., Tam, J. S., Rappaport, R., & Cheng, S.-M. (2003). Simultaneous detection subgrouping and quantitation of respiratory syncytial virus A and B by real-time PCR. Journal of Clinical Microbiology, 41(1), 149–154. https://doi.org/10.1128/JCM.41.1.149-154.2003.

    Article  Google Scholar 

  • Huang, S. J. (2010). Report on the innovation and development of China’s industrial clusters: To build the innovative capabilities. Economy & Management Publishing House.

    Google Scholar 

  • Inkpen, A. C., & Currall, S. C. (1997). International joint venture trust: An empirical examination. In B. W. Beamish & J. P. Killing (Eds.), Cooperative strategies: North American perspectives (pp. 308–334). New Lexington Press.

    Google Scholar 

  • Ionescu, A.-F., & Vernic, R. (2021). MOSOSS: An adapted multi-objective symbiotic organisms search for scheduling. Soft Computing, 25(14), 9591–9607.

    Article  Google Scholar 

  • Ireland, R. D., Hitt, M. A., & Vaidyanath, D. (2002). Alliance management as a source of competitive advantage. Journal of Management, 28(3), 413–446.

    Article  Google Scholar 

  • Jeon, J., Lee, C., & Park, Y. (2011). How to use patent information to search potential technology partners in open innovation. Journal of Intellectual Property Rights, 16(5), 385–393.

    Google Scholar 

  • Ji, G. J., Yu, M. H., & Tan, K. H. (2020). Cooperative innovation behavior based on big data. Mathematical Problems in Engineering, Article ID 4385810.

  • Ju, N. Q., & Liu, G. J. (2004). On the essence and requirement in transforming scientific advances into technological productive force from the relations between science and technology. Journal of Henan University (social Science), 44(1), 24–28. In Chinese.

    Google Scholar 

  • Jugend, D., Fiorini, P. D. C., Armellini, F., & Ferrari, A. G. (2020). Public support for innovation: A systematic review of the literature and implications for open innovation. Technological Forecasting and Social Change, 156, 119985.

    Article  Google Scholar 

  • Kale, P., Singh, H., & Perlmutter, H. (2000). Learning and protection of proprietary assets in strategic alliances: Building relational capital. Strategic Management Journal, 21(3), 217–237.

    Article  Google Scholar 

  • Kang, J., Lee, J., Jang, D., & Park, S. (2019). A methodology of partner selection for sustainable Industry-University cooperation based on LDA topic model. Sustainability, 11(12), 3478.

    Article  Google Scholar 

  • Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19(5), 461–477.

    Article  Google Scholar 

  • Laursen, K., & Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2), 131–150.

    Article  Google Scholar 

  • Leiponen, A., & Helfat, C. E. (2010). Innovation objectives, knowledge sources, and the benefits of breadth. Strategic Management Journal, 31(2), 224–236.

    Article  Google Scholar 

  • Li, M. F., Liu, G. X., & Liu, L. (2011). Empirical comparative study on the participation of enterprises and universities in Industry-university-research institute collaboration: Collaborative content, level and mode. R&D Management, 23(4), 113–118. In Chinese.

    Google Scholar 

  • Li, S. X., & Rowley, T. (2002). Inertia and evaluation mechanisms in interorganizational partner selection: Syndicate formation among US investment banks. Academy of Management Journal, 45(6), 1104–1119.

    Google Scholar 

  • Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031.

    Article  Google Scholar 

  • Littler, D., Leverick, F., & Bruce, M. (1995). Factors affecting the process of collaborative product development: A study of UK manufacturers of information and communications technology products. Journal of Product Innovation Management, 12(1), 16–32.

    Article  Google Scholar 

  • Liu, H. B., & Wang, C. M. (2005). Enterprises strategy alliance and model for cooperative partner appraising and choosing. Science Technology and Industry, 5(12), 21-23+27. In Chinese.

    Google Scholar 

  • Liu, J., & Sun, W. (2017). Discovery of potential scientific and technical collaborative relationship based on link prediction. Information Studies: Theory & Application, 40(7), 88-921+21. In Chinese.

    Google Scholar 

  • Liu, L. J., Xie, Y. L., Dai, Z. X., Zhuo, C. S., & Wu, Y. S. (2014). Establishment and clinical application of chemiluminescence immunoassay for Hepatitis C Virus antibody. Letters in Biotechnology, 25(1), 97–99. In Chinese.

    Article  Google Scholar 

  • Liu, Z. Q., Xu, H. Y., Luo, R., Dong, K., & Zhu, L. J. (2019). Research on scientific and technological interaction patterns based on topic relevance analysis. Journal of the China Society for Scientific and Technical Information, 38(10), 997–1011. In Chinese.

    Google Scholar 

  • Lo, S. S. (2009). Scientific linkage of science research and technology development: A case of genetic engineering research. Scientometrics, 82(1), 109–120.

    Article  MathSciNet  Google Scholar 

  • Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31(6), 602–628.

    Google Scholar 

  • Maraut, S., & Martínez, C. (2014). Identifying author–inventors from Spain: Methods and a first insight into results. Scientometrics, 101(1), 445–476.

    Article  Google Scholar 

  • Meyer, M. (2000). Does science push technology? Patents citing scientific literature. Research Policy, 29(3), 409–434.

    Article  Google Scholar 

  • Narin, F., Hamilton, K. S., & Olivastro, D. (1997). The increasing linkage between US technology and science. Research Policy, 26(3), 317–330.

    Article  Google Scholar 

  • Nguyen, H.-K. (2021). Applications optimal math model to solve difficult problems for businesses producing and processing agricultural products in vietnam. Axioms, 10(2), 90.

    Article  Google Scholar 

  • Nielsen, B. B. (2003). An empirical investigation of the drivers of international strategic alliance formation. European Management Journal, 21(3), 301–322.

    Article  Google Scholar 

  • Ottonicar, S. L. C., Arraiza, P. M., & Fabiano, A. (2020). Opening science and innovation: Opportunities for emerging economies. Forsajt (фopcaйт), 14(4), 95–111.

    Google Scholar 

  • Park, I., Jeong, Y., Yoon, B., & Mortara, L. (2015). Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis. Technology Analysis & Strategic Management, 27(7), 759–781.

    Article  Google Scholar 

  • Petruzzelli, A. M. (2011). The impact of technological relatedness, priorties, and geographical distance on university-industry collaborations: A joint-patent analysis. Technovation, 31(7), 309–319.

    Google Scholar 

  • Qi, Y. (2019). Research on the analytical method of science technology relationship evolution in specific fields based on patent citation analysis——Taking HCV as an example. Library and Information Service, 63(23), 97–105. In Chinese.

    MathSciNet  Google Scholar 

  • Qi, Y., Hu, Z. Y., Xiang, B., Liu, C. J., Xu, H. Y., & Wen, Y. (2019). Identifying and evaluating strategic partners for collaborative innovation: A method based on a topic analysis of papers and patents, In Proceedings of the international conference on scientometrics and informetrics (ISSI2019) (pp. 2744–2745), II.

  • Rajalo, S., & Vadi, M. (2021). Collaboration potential between low-capacity SMEs and academic researchers determined by symmetry of motivation. Technovation, 107, 102304.

    Article  Google Scholar 

  • Rothaermel, F. T. (2001). Incumbent’s advantage through exploiting complementary assets via inter firm cooperation. Strategic Management Journal, 22(6–7), 687–699.

    Article  Google Scholar 

  • Schmoch, U. (1997). Indicators and the relations between science and technology. Scientometrics, 38(1), 103–116.

    Article  Google Scholar 

  • Shi, B. S., Zhang, X. N., Gu, S. M., Tian, D., Yuan, Z. H., & Hu, Y. W. (2008). Establishment of a random PCR method for detecting unknown virus. Journal of Microbes and Infections, 3, 134-137+146. In Chinese.

    Google Scholar 

  • Song, B., Seol, H., & Park, Y. (2016). A patent portfolio-based approach for assessing potential R&D partners: An application of the Shapley value. Technological Forecasting & Social Change, 103, 156–165.

    Article  Google Scholar 

  • Sun, X. L., & Ding, K. (2018). Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents. Scientometrics, 116(3), 1735–1748.

    Article  Google Scholar 

  • Tian, B. L. (2014). Research on strategic alliance partner selection of high-tech enterprises. Yangzhou University. In Chinese.

    Google Scholar 

  • Tran, D.-H., Cheng, M.-Y., & Prayogo, D. (2016). A novel multiple objective symbiotic organisms search (MOSOS) for time-cost-labor utilization tradeoff problem. Knowledge-Based Systems, 94, 132–145.

    Article  Google Scholar 

  • University of California. (2015, September 15). What is continuous innovation. Retrieved January 7, 2022, from https://blink.ucsd.edu/sponsor/innovate/whatis.html#:~:text=Noun%3A%20modest%2C%20incremental%2C%20ongoing,activities%2C%20operations%2C%20and%20creations.

  • Verbeek, A., Debackere, K., & Luwel, M. (2003). Science cited in patents: A geographic ‘flow’ analysis of bibliographic citation patterns in patents. Scientometrics, 58(2), 241–263.

    Article  Google Scholar 

  • Verbeek, A., Debackere, K., Luwel, M., Andries, P., Zimmermann, E., & Deleus, F. (2002). Linking science to technology: Using bibliographic references in patents to build linkage schemes. Scientometrics, 54(3), 399–420.

    Article  Google Scholar 

  • Veugelers, R. (1997). Internal R&D expenditures and external technology sourcing. Research Policy, 26(3), 303–315.

    Article  Google Scholar 

  • Wang, G. B., & Guan, J. C. (2011). Measuring science-technology interactions using patent citations and author–inventor links: An exploration analysis from Chinese nanotechnology. Journal of Nanoparticle Research, 13(12), 6245–6262.

    Article  Google Scholar 

  • Wang, H. H., Xie, F. J., & Zhou, S. (2014). ‘Four-dimensional’ collaborative framework for implementing innovation-driven development strategy in China from the perspective of innovation ecosystem. Science & Technology Progress and Policy, 31(17), 7–11. In Chinese.

    Google Scholar 

  • Wang, J. (2016). Research on partner selection of industry-university-research-application in the Internet of things. Shanghai University of Engineering Technology. In Chinese.

    Google Scholar 

  • Wang, J. F., Yang, S. S., & Wang, Y. D. (2016). Research on ecological innovation development path of technology park based on grounded theory. Science & Technology Progress and Policy, 33(18), 77–85. In Chinese.

    Google Scholar 

  • Wang, L., & Huang, Z. (2020). Research on the synergetic innovation between pharmaceutical enterprises and scientific research institutions based on the quantum game. IEEE Access, 8, 63718–63724.

    Article  Google Scholar 

  • Wang, X. P. (2020). Research on the identification and evolution of science-technology topic association. Wuhan University. In Chinese.

    Google Scholar 

  • Wang, X. F., Fu, Y., Qiu, P. J., & Liu, Y. Q. (2015). Identification of R&D partners based on SAO analysis. Science Research Management, 36(10), 19–27. In Chinese.

    Google Scholar 

  • Wang, X. F., Qiao, Y. L., Hou, Y. J., Zhang, S., & Han, X. T. (2021). Measuring Technology Complementarity Between Enterprises With an hLDA Topic Model. IEEE Transactions on Engineering Management, 68(5), 1309–1320.

    Article  Google Scholar 

  • Wang, X. F., Wang, Z. N., Huang, Y., Liu, Y. Q., Zhang, J., Heng, X. F., & Zhu, D. H. (2017). Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern. Technology Analysis & Strategic Management, 29(10), 1167–1180.

    Article  Google Scholar 

  • Wang, Y., Bai, H., Stanton, M., Chen, W. Y., & Chang, E. Y. (2009). PLDA: Parallel latent dirichlet allocation for large-scale applications. Lecture Notes in Computer Science, 5564, 301–314.

    Article  Google Scholar 

  • Wang, Z. B., Han, W. M., Sun, Z. M., & Pan, X. L. (2019). Research on scientific collaboration prediction based on the combination of network topology and node attributes. Information Studies: Theory & Application, 42(8), 116-120+109. In Chinese.

    Article  Google Scholar 

  • Wen, J., & Kobayashi, S. (2001). Exploring collaborative R&D network: Some new evidence in Japan. Research Policy, 30(8), 1309–1319.

    Article  Google Scholar 

  • Wu, W. Y., Shih, H. A., & Chan, H. C. (2009). The analytic network process for partner selection criteria in strategic alliances. Expert Systems with Applications, 36(3), 4646–4653.

    Article  Google Scholar 

  • Xu, H. Y., Wei, L., Pang, H. S., Guo, T., & Fang, S. (2016). Methods to identify potential Industry-University-Research institutions cooperation partners. Journal of the China Society for Scientific and Technical Information, 35(5), 521–529. In Chinese.

    Google Scholar 

  • Xu, S., Zhai, D. S., Wang, F. F., An, X., Pang, H. S., & Sun, Y. R. (2019). A novel method for topic linkages between scientific publications and patents. Journal of the Association for Information Science & Technology, 70(9), 1026–1042.

    Article  Google Scholar 

  • Yan, E., & Guns, R. (2014). Predicting and recommending collaborations: An author-, institution-, and country-level analysis. Journal of Informetrics, 8(2), 295–309.

    Article  Google Scholar 

  • Yang, Z. Q. (2019). Research on process and driving factors of knowledge complementarity about Industry-university-research institute based on interaction of “Ba.” Chongqing University of Post and Telecommunications. In Chinese.

    Google Scholar 

  • Yin, Y. Y. (2012). Interactive study of science and technology based on citation relations between papers and patents——Case study of 3D display. Library and Information Service, 56(16), 65–70. In Chinese.

    Google Scholar 

  • Yu, C. M., Gong, Y. T., Zhao, X. L., & An, L. (2017). Collaboration recommendation of finance research based on multi-feature fusion. Data Analysis and Knowledge Discovery, 1(8), 39–47. In Chinese.

    Google Scholar 

  • Zhang, Y., Porter, A. L., Hu, Z. Y., Guo, Y., & Newman, N. C. (2014). ‘Term clumping’ for technical intelligence: A case study on dye-sensitized solar cells. Technological Forecasting & Social Change, 85, 26–39.

    Article  Google Scholar 

  • Zhang, Y. W., Wu, J., Li, P., Wu, X. J., & Zhou, X. (2015). Partner selection of industry-university-research collaboration based on two-sided matching from the perspective of innovation capacity. Journal of Jiangsu University of Science and Technology (natural Science Edition), 29(5), 488–495. In Chinese.

    Google Scholar 

  • Zhou, T., Lü, L., & Zhang, Y.-C. (2009). Predicting missing links via local information. The European Physical Journal B, 71(4), 623–630.

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The work was supported by the Fundamental Research Funds for the Central Universities—PUMC Youth Fund (Grant No. 2017330008) and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2017PT63008). In addition, we thank the anonymous reviewers for valuable comments and suggestions which helped us to improve this paper.

Author information

Authors and Affiliations

  1. Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences/Peking Union Medical College (CAMS&PUMC), No. 3, Yabao Road, Beijing, 100020, China

    Yan Qi & Ran Zhang

  2. Chengdu Library and Information Center, Chinese Academy of Sciences, No. 16, South Section 2, Yihuan Road, Chengdu, 610041, China

    Xin Zhang, Zhengyin Hu, Bin Xiang & Shu Fang

Authors
  1. Yan Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengyin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ran Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shu Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yan Qi or Xin Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, Y., Zhang, X., Hu, Z. et al. Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction. Scientometrics 127, 5519–5550 (2022). https://doi.org/10.1007/s11192-022-04306-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04306-9

Keywords

Search

Navigation