AGSTA-NET: adaptive graph spatiotemporal attention network for citation count prediction
Author:
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/s11192-022-04541-0.pdf
Reference45 articles.
1. Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499. https://doi.org/10.1016/j.joi.2019.02.011
2. Aksnes, D. W. (2003). Characteristics of highly cited papers. Research Evaluation, 12(3), 159–170. https://doi.org/10.3152/147154403781776645
3. Bhat, H. S., Huang, L. H., Rodriguez, S., Dale, R., & Heit, E. (2016). citation prediction using diverse features. IEEE International Conference on Data Mining Workshop. https://doi.org/10.1109/ICDMW.2015.131
4. Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2014). Towards a stratified learning approach to predict future citation counts. IEEE/ACM Joint Conference on Digital Libraries. https://doi.org/10.1109/JCDL.2014.6970190
5. Chan, H. F., Mixon, F. G., & Torgler, B. (2018). Relation of early career performance and recognition to the probability of winning the nobel prize in economics. Scientometrics, 114, 1069–1086. https://doi.org/10.1007/s11192-017-2614-5
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