mlsanityData to 7 Oct 2026

Structured Citation Trend Prediction Using Graph Neural Networks

arXiv 2104.02562 ↗First posted 6 Apr 2021cs.LG cs.SIcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 6 Apr 2021.

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12 of 19 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  6. 2018Predicting citation counts based on deep neural network learning techniquescited by 21
  7. 2015Predicting the long-term citation impact of recent publicationscited by 8
  8. 2020Predicting the Citations of Scholarly Papercited by 7
  9. 2019Predicting long-term publication impact through a combination of early citations and journal impact factorcited by 6
  10. 2016Leveraging Citation Networks to Visualize Scholarly Influence Over Timecited by 3
  11. 2013Which 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)cited by 1
  12. 2013Author Impact Factor: tracking the dynamics of individual scientific impactcited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 3; 2025: 2.

11 papers.

  1. 2024LEGO-Learn: Label-Efficient Graph Open-Set Learningcited by 10
  2. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  3. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  4. 2023Transferability of Graph Neural Networks using Graphon and Sampling Theoriescited by 2
  5. 2021Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Predictioncited by 1
  6. 2022Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graphcited by 1
  7. 2022Artificial intelligence technologies to support research assessment: A reviewcited by 1
  8. 2023Graph Neural Networks For Mapping Variables Between Programs -- Extended Versioncited by 0
  9. 2024CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimationcited by 0
  10. 2025Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognitioncited by 0
  11. 2025Complex-Weighted Convolutional Networks: Provable Expressiveness via Complex Diffusioncited by 0