Predicting publication productivity for authors: Shallow or deep architecture?

Wumei Du, Zheng Xie () and Yiqin Lv
Additional contact information
Wumei Du: National University of Defense Technology
Zheng Xie: National University of Defense Technology
Yiqin Lv: National University of Defense Technology

Scientometrics, 2021, vol. 126, issue 7, No 20, 5855-5879

Abstract: Abstract Academic administrators and funding agencies must predict the publication productivity of research groups and individuals to assess authors’ abilities. However, such prediction remains an elusive task due to the randomness of individual research and the diversity of authors’ productivity patterns. We applied two kinds of approaches to this prediction task: deep neural network learning and model-based approaches. We found that a neural network cannot give a good long-term prediction for groups, while the model-based approaches cannot provide short-term predictions for individuals. We proposed a model that integrates the advantages of both data-driven and model-based approaches, and the effectiveness of this method was validated by applying it to a high-quality dblp dataset, demonstrating that the proposed model outperforms the tested data-driven and model-based approaches.

Keywords: Scientific publications; Productivity prediction; Data model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-04027-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:7:d:10.1007_s11192-021-04027-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-04027-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().