# Structured Citation Trend Prediction Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2104.02562 · HTML: https://mlsanity.com/paper/2104.02562/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Predicting citation counts based on deep neural network learning techniques](https://mlsanity.com/paper/1809.04365/) (2018)
- [Predicting the long-term citation impact of recent publications](https://mlsanity.com/paper/1503.09156/) (2015)
- [Predicting the Citations of Scholarly Paper](https://mlsanity.com/paper/2008.05013/) (2020)
- [Predicting long-term publication impact through a combination of early citations and journal impact factor](https://mlsanity.com/paper/1909.08907/) (2019)
- [Leveraging Citation Networks to Visualize Scholarly Influence Over Time](https://mlsanity.com/paper/1611.07135/) (2016)
- [Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P100)](https://mlsanity.com/paper/1306.4454/) (2013)
- [Author Impact Factor: tracking the dynamics of individual scientific impact](https://mlsanity.com/paper/1312.2650/) (2013)

## Built on by (most cited first, 11 of 11)

- [LEGO-Learn: Label-Efficient Graph Open-Set Learning](https://mlsanity.com/paper/2410.16386/) (2024)
- [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023)
- [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024)
- [Transferability of Graph Neural Networks using Graphon and Sampling Theories](https://mlsanity.com/paper/2307.13206/) (2023)
- [Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction](https://mlsanity.com/paper/2104.06046/) (2021)
- [Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph](https://mlsanity.com/paper/2210.00450/) (2022)
- [Artificial intelligence technologies to support research assessment: A review](https://mlsanity.com/paper/2212.06574/) (2022)
- [Graph Neural Networks For Mapping Variables Between Programs -- Extended Version](https://mlsanity.com/paper/2307.13014/) (2023)
- [CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation](https://mlsanity.com/paper/2402.05135/) (2024)
- [Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04963/) (2025)
- [Complex-Weighted Convolutional Networks: Provable Expressiveness via Complex Diffusion](https://mlsanity.com/paper/2511.13937/) (2025)

## Abstract (arXiv)

Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.