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Predicting citation counts based on deep neural network learning techniques

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Cited by:

  1. Qingnan Xie & Richard B. Freeman, 2020. "The Contribution of Chinese Diaspora Researchers to Global Science and China's Catching Up in Scientific Research," NBER Working Papers 27169, National Bureau of Economic Research, Inc.
  2. Kehan Wang & Wenxuan Shi & Junsong Bai & Xiaoping Zhao & Liying Zhang, 2021. "Prediction and application of article potential citations based on nonlinear citation-forecasting combined model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6533-6550, August.
  3. Soroush Taheri & Sadegh Aliakbary, 2022. "Research trend prediction in computer science publications: a deep neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 849-869, February.
  4. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
  5. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
  6. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
  7. Sato, Ryoma & Yamada, Makoto & Kashima, Hisashi, 2022. "Poincare: Recommending Publication Venues via Treatment Effect Estimation," Journal of Informetrics, Elsevier, vol. 16(2).
  8. Xie, Zheng, 2020. "Predicting publication productivity for researchers: A piecewise Poisson model," Journal of Informetrics, Elsevier, vol. 14(3).
  9. José Satsumi López-Morales & Héctor Francisco Salazar-Núñez & Claudia Guadalupe Zarrabal-Gutiérrez, 2022. "The impact of qualitative methods on article citation: an international business research perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3225-3236, June.
  10. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).
  11. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
  12. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
  13. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
  14. Saarela, Mirka & Kärkkäinen, Tommi, 2020. "Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator," Journal of Informetrics, Elsevier, vol. 14(2).
  15. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
  16. Wang, Xing & Zhang, Zhihui, 2020. "Improving the reliability of short-term citation impact indicators by taking into account the correlation between short- and long-term citation impact," Journal of Informetrics, Elsevier, vol. 14(2).
  17. Avick Kumar Dey & Pijush Kanti Dutta Pramanik & Prasenjit Choudhury & Goutam Bandopadhyay, 2021. "Distinctive author ranking using DEA indexing," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 601-620, April.
  18. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
  19. Li, Xin & Ma, Xiaodi & Feng, Ye, 2024. "Early identification of breakthrough research from sleeping beauties using machine learning," Journal of Informetrics, Elsevier, vol. 18(2).
  20. Zhou, Yuhao & Wang, Ruijie & Zeng, An & Zhang, Yi-Cheng, 2020. "Identifying prize-winning scientists by a competition-aware ranking," Journal of Informetrics, Elsevier, vol. 14(3).
  21. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
  22. Chowdhury, K.P., 2021. "Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers," Journal of Informetrics, Elsevier, vol. 15(1).
  23. 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.
  24. Croft, William L. & Sack, Jörg-Rüdiger, 2022. "Predicting the citation count and CiteScore of journals one year in advance," Journal of Informetrics, Elsevier, vol. 16(4).
  25. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  26. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
  27. Klemiński, Rajmund & Kazienko, Przemyslaw & Kajdanowicz, Tomasz, 2021. "Where should I publish? Heterogeneous, networks-based prediction of paper’s citation success," Journal of Informetrics, Elsevier, vol. 15(3).
  28. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
  29. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
  30. 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.
  31. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
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