# Longitudinal Citation Prediction using Temporal Graph Neural Networks

arXiv: https://arxiv.org/abs/2012.05742 · HTML: https://mlsanity.com/paper/2012.05742/

## Key facts

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

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

- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [ResearchTown: Simulator of Human Research Community](https://mlsanity.com/paper/2412.17767/) (2024)
- [CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding](https://mlsanity.com/paper/2105.10912/) (2021)
- [Determining the Credibility of Science Communication](https://mlsanity.com/paper/2105.14473/) (2021)
- [Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks](https://mlsanity.com/paper/2408.15371/) (2024)
- [Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph](https://mlsanity.com/paper/2210.00450/) (2022)
- [LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction](https://mlsanity.com/paper/2610.10138/) (2026)
- [Local Citation Recommendation Without Graph Signals](https://mlsanity.com/paper/2610.15832/) (2026)

## Abstract (arXiv)

Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time. Prior work viewed this as a static prediction task. As papers and their citations evolve over time, considering the dynamics of the number of citations a paper will receive would seem logical. Here, we introduce the task of sequence citation prediction. The goal is to accurately predict the trajectory of the number of citations a scholarly work receives over time. We propose to view papers as a structured network of citations, allowing us to use topological information as a learning signal. Additionally, we learn how this dynamic citation network changes over time and the impact of paper meta-data such as authors, venues and abstracts. To approach the new task, we derive a dynamic citation network from Semantic Scholar spanning over 42 years. We present a model which exploits topological and temporal information using graph convolution networks paired with sequence prediction, and compare it against multiple baselines, testing the importance of topological and temporal information and analyzing model performance. Our experiments show that leveraging both the temporal and topological information greatly increases the performance of predicting citation counts over time.