Diversity of temporal influence in popularity prediction of scientific publications

Yanbo Zhou, Hongbing Cheng, Qu Li and Weihong Wang ()
Additional contact information
Yanbo Zhou: Zhejiang University of Technology
Hongbing Cheng: Zhejiang University of Technology
Qu Li: Zhejiang University of Technology
Weihong Wang: Zhejiang University of Technology

Scientometrics, 2020, vol. 123, issue 1, No 18, 383-392

Abstract: Abstract Predicting the future influential papers is a challenging issue witch has attracted many attentions. In this paper, we focused on the temporal information of citations to study the popularity prediction problem from the perspective of citation dynamics. The experimental study of the APS citation data shows that the temporal decay rate of the influence of citations is decay with paper’s age, and the decay rate is a power-law distribution. We introduced the diversity of temporal decay rate of the influence of citations to predict the future popularity of papers, and proposed a diverse temporal decay method. The result shows that this method can improve the prediction accuracy compared with other popularity-based prediction methods. More importantly, this method can detect some of the newly published papers that haven’t accumulated many citations but will quickly become popular in the future.

Keywords: Popularity prediction; Temporal decay; Citation dynamics; Scientific impact (search for similar items in EconPapers)
Date: 2020
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/s11192-020-03354-3 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:123:y:2020:i:1:d:10.1007_s11192-020-03354-3

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

DOI: 10.1007/s11192-020-03354-3

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 ().