# Predicting the Citations of Scholarly Paper

arXiv: https://arxiv.org/abs/2008.05013 · HTML: https://mlsanity.com/paper/2008.05013/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

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

- [Structured Citation Trend Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2104.02562/) (2021)
- [Automatic Evaluation Metrics for Artificially Generated Scientific Research](https://mlsanity.com/paper/2503.05712/) (2025)
- [CiteBART: Learning to Generate Citations for Local Citation Recommendation](https://mlsanity.com/paper/2412.17534/) (2024)
- [Is Peer-Reviewing Worth the Effort?](https://mlsanity.com/paper/2412.14351/) (2024)
- [Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph](https://mlsanity.com/paper/2210.00450/) (2022)
- [A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles](https://mlsanity.com/paper/2104.04939/) (2021)
- [Number and quality of diagrams in scholarly publications is associated with number of citations](https://mlsanity.com/paper/2104.14815/) (2021)
- [LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction](https://mlsanity.com/paper/2610.10138/) (2026)

## Abstract (arXiv)

Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper impact decaying over time, early citations, and early citers' impact. In addition, by analyzing factors that drive citation growth, we propose a multi-feature model for impact prediction. Experimental results demonstrate that the two models improve the accuracy in predicting scholarly paper citations. Compared to the multi-feature model, the PPI model yields superior predictive performance in terms of range-normalized RMSE. The PPI model better interprets the changes in citation, without the need to adjust parameters. Compared to the PPI model, the multi-feature model performs better prediction in terms of Mean Absolute Percentage Error and Accuracy; however, their predictive performance is more dependent on the parameter adjustment.