Optimizing collaboration decisions in technological innovation through machine learning: identify trend and partners in collaboration-knowledge interdependent networks
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10479-024-05867-z.pdf
Reference67 articles.
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3. Basu, S., & Maulik, U. (2018). Link prediction in complex dynamic networks using multiple interdependent time series. In International conference on computing, power and communication technologies (GUCON) (pp. 1136–1141). IEEE.
4. Boh, W. F., Evaristo, R., & Ouderkirk, A. (2014). Balancing breadth and depth of expertise for innovation: A 3M story. Research. Policy, 43(2), 349–366. https://doi.org/10.1016/j.respol.2013.10.009
5. Brennecke, J., & Rank, O. (2017). The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study. Research Policy, 46(4), 768–783. https://doi.org/10.1016/j.respol.2017.02.002
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