Prediction of citation dynamics of individual papers
Michael Golosovsky
TL;DR
This paper uses a stochastic model to forecast the citation trajectories of individual papers, providing both estimates and probabilistic margins for future citations.
Contribution
It applies a previously developed stochastic model to predict citation dynamics and quantify uncertainty in those predictions for individual papers.
Findings
Successful prediction of citation trajectories
Quantification of uncertainty in citation forecasts
Application of stochastic modeling to citation data
Abstract
We apply stochastic model of citation dynamics of individual papers developed in our previous work (M. Golosovsky and S. Solomon, Phys. Rev. E\textbf{ 95}, 012324 (2017)) to forecast citation career of individual papers. We focus not only on the estimate of the future citations of a paper but on the probabilistic margins of such estimate as well.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Complex Network Analysis Techniques · scientometrics and bibliometrics research