Predicting authors’ citation counts and h-indices with a neural network

Tobias Mistele, Tom Price and Sabine Hossenfelder ()
Additional contact information
Tobias Mistele: Frankfurt Institute for Advanced Studies
Tom Price: Frankfurt Institute for Advanced Studies
Sabine Hossenfelder: Frankfurt Institute for Advanced Studies

Scientometrics, 2019, vol. 120, issue 1, No 5, 87-104

Abstract: Abstract We here describe and present results of a simple neural network that predicts individual researchers’ future citation counts based on a variety of data from the researchers’ past. For publications available on the open access-server arXiv.org we find a higher predictability than previous studies.

Keywords: Neural network; h-index; Arxiv; Citation metrics (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-019-03110-2 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:120:y:2019:i:1:d:10.1007_s11192-019-03110-2

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

DOI: 10.1007/s11192-019-03110-2

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 ().