Detecting technological recombination using semantic analysis and dynamic network analysis
Author:
Funder
National Nature Science Foundation of China Funds
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Computer Science Applications,General Social Sciences
Link
https://link.springer.com/content/pdf/10.1007/s11192-023-04812-4.pdf
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3. Barros, C. D., Mendonça, M. R., Vieira, A. B., & Ziviani, A. (2021). A survey on embedding dynamic graphs. ACM Computing Surveys (CSUR), 55(1), 1–37.
4. Chen, J., Zhang, J., Xu, X., Fu, C., Zhang, D., Zhang, Q., & Xuan, Q. (2019). E-lstm-d: A deep learning framework for dynamic network link prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3699–3712.
5. Chen, X., Ye, P., Huang, L., Wang, C., Cai, Y., Deng, L., & Ren, H. (2023). Exploring science-technology linkages: A deep learning-empowered solution. Information Processing & Management, 60(2), 103255.
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