# Predicting citation counts based on deep neural network learning techniques

arXiv: https://arxiv.org/abs/1809.04365 · HTML: https://mlsanity.com/paper/1809.04365/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 21 of 21)

- [Structured Citation Trend Prediction Using Graph Neural Networks](https://mlsanity.com/paper/2104.02562/) (2021)
- [Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction](https://mlsanity.com/paper/2305.17417/) (2023)
- [CiteBART: Learning to Generate Citations for Local Citation Recommendation](https://mlsanity.com/paper/2412.17534/) (2024)
- [H2CGL: Modeling Dynamics of Citation Network for Impact Prediction](https://mlsanity.com/paper/2305.01572/) (2023)
- [Predicting article quality scores with machine learning: The UK Research Excellence Framework](https://mlsanity.com/paper/2212.05415/) (2022)
- [A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence](https://mlsanity.com/paper/2402.12928/) (2024)
- [CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text](https://mlsanity.com/paper/2410.04404/) (2024)
- [Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement](https://mlsanity.com/paper/2311.09262/) (2023)
- [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)
- [Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network](https://mlsanity.com/paper/2210.06346/) (2022)
- [Artificial intelligence technologies to support research assessment: A review](https://mlsanity.com/paper/2212.06574/) (2022)
- [Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings](https://mlsanity.com/paper/2401.15713/) (2024)
- [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)
- [Predicting the Citation Count and CiteScore of Journals One Year in Advance](https://mlsanity.com/paper/2210.12908/) (2022)
- [Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics](https://mlsanity.com/paper/2403.18838/) (2024)
- [Focus Where It Matters: Graph Selective State Focused Attention Networks](https://mlsanity.com/paper/2410.15849/) (2024)
- [A Causal Inference Approach for Quantifying Research Impact](https://mlsanity.com/paper/2503.13485/) (2025)
- [Benchmark Datasets for Lead-Lag Forecasting on Social Platforms](https://mlsanity.com/paper/2511.03877/) (2025)
- [LLM-Metrics: Measuring Research Impact Through Large Language Model Memory](https://mlsanity.com/paper/2605.22176/) (2026)

## Abstract (arXiv)

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics and bibliometrics establish quantified analysis methods and measurements for scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citations prediction model, we employed artificial neural networks which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method out-performs state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.