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.
Aksnes, D. W. (2003). Characteristics of highly cited papers. Research Evaluation, 12(3), 159–170.
Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485–499.
Bai, X., Zhang, F., & Lee, I. (2019). Predicting the citations of scholarly paper. Journal of Informetrics, 13(1), 407–418.
Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
Article MathSciNet Google Scholar
Behrouzi, S., Sarmoor, Z. S., Hajsadeghi, K., & Kavousi, K. (2020). Predicting scientific research trends based on link prediction in keyword networks. Journal of Informetrics, 14(4), Article 101079.
Bertsimas, D., Brynjolfsson, E., Reichman, S., & Silberholz, J. (2013). Network analysis for predicting academic impact. In ICIS.
Billah, S. M., & Gauch, S. (2015, November). Social network analysis for predicting emerging researchers. In 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K) (Vol. 1, pp. 27–35). IEEE.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
Blümel, C., & Schniedermann, A. (2020). Studying review articles in scientometrics and beyond: A research agenda. Scientometrics, 124, 711–728.
Bollen, J., Rodriquez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69, 669–687.
Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.
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.
Bornmann, L., & Marx, W. (2015). Methods for the generation of normalized citation impact scores in bibliometrics: Which method best reflects the judgements of experts? Journal of Informetrics, 9(2), 408–418.
Braun, T., Bergstrom, C. T., Frey, B. S., Osterloh, M., West, J. D., Pendlebury, D., & Rohn, J. (2010). How to improve the use of metrics. Nature, 465(17), 870–872.
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.
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.
Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386–1409.
Chen, H., Zhang, G., Zhu, D., & Lu, J. (2017). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technological Forecasting and Social Change, 119, 39–52.
Chen, P., Xie, H., Maslov, S., & Redner, S. (2007). Finding scientific gems with Google’s PageRank algorithm. Journal of Informetrics, 1(1), 8–15.
Chen, S., Arsenault, C., & Larivière, V. (2015). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034–1046.
Cheng, Q., Wang, J., Lu, W., Huang, Y., & Bu, Y. (2020). Keyword-citation-keyword network: A new perspective of discipline knowledge structure analysis. Scientometrics, 124, 1923–1943.
Choi, J., Yi, S., & Lee, K. C. (2011). Analysis of keyword networks in MIS research and implications for predicting knowledge evolution. Information & Management, 48(8), 371–381.
Chung, P., & Sohn, S. Y. (2020). Early detection of valuable patents using a deep learning model: Case of semiconductor industry. Technological Forecasting and Social Change, 158, Article 120146.
Cole, S., Cole, J. R., & Simon, G. A. (1981). Chance and consensus in peer review. Science, 214(4523), 881–886.
Cronin, B. (1984). The citation process: The role and significance of citations in scientific communication. Taylor Graham.
de Winter, J. (2024). Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among scientific abstracts. Scientometrics. https://doi.org/10.1007/s11192-024-04939-y
Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861–873.
Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., & Zhai, C. (2014). Content-based citation analysis: The next generation of citation analysis. Journal of the Association for Information Science and Technology, 65(9), 1820–1833.
Dvořáčková, L., Joachimiak, M. P., Černý, M., Kubecová, A., Sklenák, V., & Kliegr, T. (2025). Explaining word embeddings with perfect fidelity: A case study in predicting research impact. Machine Learning, 114(12), 1–28.
Article MathSciNet Google Scholar
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.
Article MathSciNet Google Scholar
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308.
Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95, 225–242.
Ernst, E., & Resch, K. L. (1993). Fibrinogen as a cardiovascular risk factor: A meta-analysis and review of the literature. Annals of Internal Medicine, 118(12), 956–963.
Eubanks, D. L., Palanski, M. E., Swart, J., Hammond, M. M., & Oguntebi, J. (2014). Time to create: Pathways to earlier and later creative discoveries in Nobel prize winners. Creativity and leadership in science, technology, and innovation (pp. 184–208). Routledge.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Fu, L., & Aliferis, C. (2010). Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientometrics, 85(1), 257–270.
Fu, L. D., & Aliferis, C. (2008). Models for predicting and explaining citation count of biomedical articles. In AMIA Annual Symposium Proceedings 2008 (p.222).
Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.
Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1 (4), 359–375.
Garfield, E. (2009). From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics, 3(3), 173–179.
González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379–391.
Google Scholar Blog. (2011). Google Scholar citations open to all. Published 16 November 2011. Retrieved June 20, 2016, from https://scholar.googleblog.com/2011/11/google-scholar-citations-open-to-all.html
Gross, P. L., & Gross, E. M. (1927). College libraries and chemical education. Science, 66(1713), 385–389.
Gu, X., & Krenn, M. (2024). Forecasting high-impact research topics via machine learning on evolving knowledge graphs. arXiv preprint arXiv:2402.08640.
Haghighat, M., & Hayatdavoudi, J. (2021). How hot are hot papers? The issue of prolificacy and self-citation stacking. Scientometrics, 126(1), 565–578.
Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., Whelan, J., & Wilson, S. (2008). What makes an article influential? Predicting impact in social and personality psychology. Scientometrics, 76(1), 169–185.
Hirsch, J. E. (2007). Does the h index have predictive power? Proceedings of the National Academy of Sciences, 104(49), 19193–19198.
Hofmann, T. (1999, August). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 50–57).
Hou, J., Pan, H., Guo, T., Lee, I., Kong, X., & Xia, F. (2019). Prediction methods and applications in the science of science: A survey. Computer Science Review, 34, Article 100197.
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