A review of scientific impact prediction: tasks, features and methods-Reference-Cited by-同舟云学术

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A review of scientific impact prediction: tasks, features and methods

Published:2022-11-26 Issue:1 Volume:128 Page:543-585
ISSN:0138-9130
Container-title:Scientometrics
language:en
Short-container-title:Scientometrics

Author:

Xia Wanjun,Li Tianrui,Li Chongshou^ORCID

Funder

Sichuan Academic Achievement Analysis and Application Research Center

Natural Science Foundation of Sichuan Province

National Natural Science Foundation of China

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/s11192-022-04547-8.pdf

Reference158 articles.

1. Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

2. Abramo, G., D’Angelo, C. A., & Felici, G. (2019a). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32–49.

3. Abramo, G., D’Angelo, C. A., & Reale, E. (2019b). Peer review versus bibliometrics: Which method better predicts the scholarly impact of publications? Scientometrics, 121(1), 537–554.

4. Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.

5. Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Predicting scientific success. Nature, 489(7415), 201–202.

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