Academic rising star prediction via scholar’s evaluation model and machine learning techniques
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DOI: 10.1007/s11192-019-03131-x
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References listed on IDEAS
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- Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Liu, Kailiang & Xu, Zhitong & Chen, Chun-houh & Nakano, Junji & Honda, Keisuke, 2023. "Article’s scientific prestige: Measuring the impact of individual articles in the web of 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.
- Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
- Chung, Jaemin & Ko, Namuk & Kim, Hyeonsu & Yoon, Janghyeok, 2021. "Inventor profile mining approach for prospective human resource scouting," Journal of Informetrics, Elsevier, vol. 15(1).
- Bin Wang & Feng Wu & Lukui Shi, 2023. "AGSTA-NET: adaptive graph spatiotemporal attention network for citation count prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 511-541, January.
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Keywords
Rising stars; Impact prediction; Academic evaluation; Classification;All these keywords.
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