Abramo, G., D’Angelo, C. A., & Felici, G. (2019). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32–49.
Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.
Acuna, D. E., Allesina, S., & Kording, K. P. (2012). Future impact: Predicting scientific success. Nature,489(7415), 201.
Bai, X. M., Zhang, L. I., & Lee, I. (2019). Predicting the citations of scholarly paper. Journal of Informetrics, 13, 407–418.
Bornmann, L., Leydesdorff, L., & Wang, J. (2014). How to improve the prediction based on citation impact percentiles for years shortly after the publication date? Journal of Informetrics, 8(1), 175–180.
Cao, X., Chen, Y., & Liu, K. R. (2016). A data analytic approach to quantifying scientific impact. Journal of Informetrics, 10(2), 471–484.
- Clauset, A., Larremore, D. B., & Sinatra, R. (2017). Data-driven predictions in the science of science. Science,355(6324), 477–480.
Paper not yet in RePEc: Add citation now
- Consul, P. C., & Jain, G. C. (1973). A generalization of the Poisson distribution. Technometrics,15(4), 791–799.
Paper not yet in RePEc: Add citation now
- Dong, Y., Johnson, R. A., & Chawla, N. V. (2016). Can scientific impact be predicted? IEEE Transactions on Big Data,2(1), 18–30.
Paper not yet in RePEc: Add citation now
Egghe, L., & Rousseau, R. (2006). An informetric model for the hirsch-index. Scientometrics,69(1), 121–129.
Ejermo, O., Fassio, C., & Källström, J. (2019). Does mobility across universities raise scientific productivity? Oxford Bulletin of Economics and Statistics, 82(3), 603–624.
- García-Suaza, A., Otero, J., & Winkelmann, R. (2019). Predicting early career productivity of PhD economists: Does advisor-match matter? Scientometrics,122, 429–449.
Paper not yet in RePEc: Add citation now
- Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science,122(3159), 108–111.
Paper not yet in RePEc: Add citation now
Harnad, S. (2009). Open access scientometrics and the UK research assessment exercise. Scientometrics,79(1), 147–156.
- Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.
Paper not yet in RePEc: Add citation now
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Paper not yet in RePEc: Add citation now
- Hollander, M., & Wolfe, D. A. (1973). Nonparametric statistical methods. Hoboken: Wiley.
Paper not yet in RePEc: Add citation now
Hu, Y. H., Tai, C. T., Liu, K. E., & Cai, C. F. (2020). Identification of highly-cited papers using topic-model-based and bibliometric features: The consideration of keyword popularity. Journal of Informetrics, 14, 101004.
Klimek, P., Jovanovic, A. S., Egloff, R., & Schneider, R. (2016). Successful fish go with the flow: Citation impact prediction based on centrality measures for term-document networks. Scientometrics,107(3), 1265–1282.
Kosteas, V. D. (2018). Predicting long-run citation counts for articles in top economics journals. Scientometrics,115(3), 1395–1412.
- Laurance, W. F., Useche, D. C., Laurance, S. G., & Bradshaw, C. J. (2013). Predicting publication success for biologists. BioScience,63(10), 817–823.
Paper not yet in RePEc: Add citation now
- Lehman, H. C. (2017). Age and achieve. Princeton: Princeton University Press.
Paper not yet in RePEc: Add citation now
Lindahl, J., Colliander, C., & Danell, R. (2020). Early career performance and its correlation with gender and publication output during doctoral education. Scientometrics,122(1), 309–330.
- Mazloumian, A. (2012). Predicting researchers’ scientific impact. Plos One, 7(11), 1–5.
Paper not yet in RePEc: Add citation now
Mistele, T., Price, T., & Hossenfelder, S. (2019). Predicting authors’ citation counts and $$h$$ h -indices with a neural network. Scientometrics,120, 87–104.
- Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines Vinod Nair. ICML (pp. 807–814).
Paper not yet in RePEc: Add citation now
- Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society. Series A-G, 135(3), 370–384.
Paper not yet in RePEc: Add citation now
- Newman, M. E. J. (2014). Prediction of highly cited papers. Europhysics Letters, 105(2), 28002.
Paper not yet in RePEc: Add citation now
- Pobiedina, N., & Ichise, R. (2016). Citation count prediction as a link prediction problem. Applied Intelligence, 44(2), 252–268.
Paper not yet in RePEc: Add citation now
- Price, D. J. S. (1965). Networks of scientific papers. Science,149(3683), 510–515.
Paper not yet in RePEc: Add citation now
Ruan, X. M., Zhu, Y. Y., Li, J., & Cheng, Y. (2020). Predicting the citation counts of individual papers via a BPneural network. Journal of Informetrics, 14, 101039.
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks,61, 85–117.
Paper not yet in RePEc: Add citation now
- Simonton, D. K. (1984). Creative productivity and age: A mathematical model based on a two-step cognitive process. Developmental Review, 4(1), 77–111.
Paper not yet in RePEc: Add citation now
- Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A. L. (2016). Quantifying the evolution of individual scientific impact. Science, 354, aaf5239.
Paper not yet in RePEc: Add citation now
Stern, D. I. (2014). High-ranked social science journal articles can be identified from early citation information. Plos ONE, 9(11), e112520.
- Wang, D., Song, C., & Barabási, A. L. (2013). Quantifying long-term scientific impact. Science,342(6154), 127–132.
Paper not yet in RePEc: Add citation now
Wang, F. H., Fan, Y., Zeng, A., & Di, Z. R. (2019). Can we predict ESI highly cited publications? Scientometrics,118, 109–125.
- Way, S. F., Morgan, A. C., Clauset, A., & Larremore, D. B. (2017). The misleading narrative of the canonical faculty productivity trajectory. Proceedings of the National Academy of Sciences of the United States of America, 114(44), 9216–9223.
Paper not yet in RePEc: Add citation now
Way, S. F., Morgan, A. C., Larremore, D. B., & Clauset, A. (2019). Productivity, prominence, and the effects of academic environment. Proceedings of the National Academy of Sciences of the United States of America, 116(22), 10729–10733.
Xie, Z. (2019). A cooperative game model for the multimodality of coauthorship networks. Scientometrics,121(1), 503–519.
Xie, Z. (2020a). Predicting publication productivity for researchers: A piecewise Poisson model. Journal of Informetrics, 14(3), 101065.
Xie, Z. (2020b). Predicting the number of coauthors for researchers: A learning model. Journal of Informetrics, 14(2), 101036.
- Xie, Z. (2020c). A prediction method of publication productivity for researchers. IEEE Transactions on Computational Social Systems. (to be accepted).
Paper not yet in RePEc: Add citation now
- Xie, Z., Li, M., Li, J. P., Duan, X. J., & Ouyang, Z. Z. (2018). Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS. EPJ Data Science,7, 1–17.
Paper not yet in RePEc: Add citation now
Xie, Z., Ouyang, Z. Z., & Li, J. P. (2016). A geometric graph model for coauthorship networks. Journal of Informetrics, 10, 299–311.
Xie, Z., Ouyang, Z. Z., Li, J. P., Dong, E. M., & Yi, D. Y. (2018). Modelling transition phenomena of scientific coauthorship networks. Journal of the Association for Information Science and Technology, 69(2), 305–317.
- Xu, J. G., Li, M. J., Jiang, J., Ge, B. F., & Cai, M. S. (2019). Early prediction of scientific impact based on multi-bibliographic features and convolutional neural network. IEEE Access,7, 92248–92258.
Paper not yet in RePEc: Add citation now
Ye Fred, Y., & Rousseauc, R. (2008). The power law model and total career $$h$$ h -index sequences. Journal of Informetrics, 2(4), 288–297.
Yu, T., Yu, G., Li, P. Y., & Wang, L. (2014). Citation impact prediction for scientific papers using stepwise regression analysis. Scientometrics,101(2), 1233–1252.