Understanding and predicting future research impact at different career stages—A social network perspective
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DOI: 10.1002/asi.24415
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Cited by:
- Liang, Zhentao & Ba, Zhichao & Mao, Jin & Li, Gang, 2023. "Research complexity increases with scientists’ academic age: Evidence from library and information science," Journal of Informetrics, Elsevier, vol. 17(1).
- Matthias Kuppler, 2022. "Predicting the future impact of Computer Science researchers: Is there a gender bias?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6695-6732, November.
- Dongyu Zang & Chunli Liu, 2023. "Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2371-2416, April.
- Orzechowski, Kamil P. & Mrowinski, Maciej J. & Fronczak, Agata & Fronczak, Piotr, 2023. "Asymmetry of social interactions and its role in link predictability: The case of coauthorship networks," Journal of Informetrics, Elsevier, vol. 17(2).
- Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
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