# Predicting the long-term citation impact of recent publications

arXiv: https://arxiv.org/abs/1503.09156 · HTML: https://mlsanity.com/paper/1503.09156/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2015.

## Built on by (most cited first, 8 of 8)

- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Structured Citation Trend Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2104.02562/) (2021)
- [Does the Market of Citations Reward Reproducible Work?](https://mlsanity.com/paper/2204.03829/) (2022)
- [Is Peer-Reviewing Worth the Effort?](https://mlsanity.com/paper/2412.14351/) (2024)
- [The Gene of Scientific Success](https://mlsanity.com/paper/2202.08461/) (2022)
- [Quantifying the Online Long-Term Interest in Research](https://mlsanity.com/paper/2209.06212/) (2022)
- [Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles](https://mlsanity.com/paper/2503.20835/) (2025)
- [GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement](https://mlsanity.com/paper/2609.26361/) (2026)

## Abstract (arXiv)

A fundamental problem in citation analysis is the prediction of the long-term citation impact of recent publications. We propose a model to predict a probability distribution for the future number of citations of a publication. Two predictors are used: The impact factor of the journal in which a publication has appeared and the number of citations a publication has received one year after its appearance. The proposed model is based on quantile regression. We employ the model to predict the future number of citations of a large set of publications in the field of physics. Our analysis shows that both predictors (i.e., impact factor and early citations) contribute to the accurate prediction of long-term citation impact. We also analytically study the behavior of the quantile regression coefficients for high quantiles of the distribution of citations. This is done by linking the quantile regression approach to a quantile estimation technique from extreme value theory. Our work provides insight into the influence of the impact factor and early citations on the long-term citation impact of a publication, and it takes a step toward a methodology that can be used to assess research institutions based on their most recently published work.