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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Must-read papers on graph neural networks (GNN)
| Date | Stars |
|---|---|
| 2026-07-31 | 16827 |
| 2026-08-01 | 16827 |
| 2026-08-05 | 16827 |
| 2026-08-06 | 16827 |
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# Must-read papers on GNN
GNN: graph neural network
Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai.
## [Content](#content)
<table>
<tr><td colspan="2"><a href="#survey-papers">1. Survey</a></td></tr>
<tr><td colspan="2"><a href="#models">2. Models</a></td></tr>
<tr>
<td> <a href="#basic-models">2.1 Basic Models</a></td>
<td> <a href="#graph-types">2.2 Graph Types</a></td>
</tr>
<tr>
<td> <a href="#pooling-methods">2.3 Pooling Methods</a></td>
<td> <a href="#analysis">2.4 Analysis</a></td>
</tr>
<tr>
<td> <a href="#efficiency">2.5 Efficiency</a></td>
<td> <a href="#explainability">2.6 Explainability</a></td>
</tr>
<tr><td colspan="2"><a href="#applications">3. Applications</a></td></tr>
<tr>
<td> <a href="#physics">3.1 Physics</a></td>
<td> <a href="#chemistry-and-biology">3.2 Chemistry and Biology</a></td>
</tr>
<tr>
<td> <a href="#knowledge-graph">3.3 Knowledge Graph</a></td>
<td> <a href="#recommender-systems">3.4 Recommender Systems</a></td>
</tr>
<tr>
<td> <a href="#computer-vision">3.5 Computer Vision</a></td>
<td> <a href="#natural-language-processing">3.6 Natural Language Processing</a></td>
</tr>
<tr>
<td> <a href="#generation">3.7 Generation</a></td>
<td> <a href="#combinatorial-optimization">3.8 Combinatorial Optimization</a></td>
</tr>
<tr>
<td> <a href="#adversarial-attack">3.9 Adversarial Attack</a></td>
<td> <a href="#graph-clustering">3.10 Graph Clustering</a></td>
</tr>
<tr>
<td> <a href="#graph-classification">3.11 Graph Classification</a></td>
<td> <a href="#reinforcement-learning">3.12 Reinforcement Learning</a></td>
</tr>
<tr>
<td> <a href="#traffic-network">3.13 Traffic Network</a></td>
<td> <a href="#few-shot-and-zero-shot-learning">3.14 Few-shot and Zero-shot Learning</a></td>
</tr>
<tr>
<td> <a href="#program-representation">3.15 Program Representation</a></td>
<td> <a href="#social-network">3.16 Social Network</a></td>
</tr>
<tr>
<td> <a href="#graph-matching">3.17 Graph Matching</a></td>
<td> <a href="#computer-network">3.18 Computer Network</a></td>
</tr>
</table>
## [Survey papers](#content)
1. **Introduction to Graph Neural Networks.** Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020. [book](https://www.morganclaypool.com/doi/10.2200/S00980ED1V01Y202001AIM045)
*Zhiyuan Liu, Jie Zhou.*
1. **Graph Neural Networks: A Review of Methods and Applications.** AI Open 2020. [paper](https://doi.org/10.1016/j.aiopen.2021.01.001)
*Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.*
1. **A Comprehensive Survey on Graph Neural Networks.** arxiv 2019. [paper](https://arxiv.org/pdf/1901.00596.pdf)
*Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.*
1. **Adversarial Attack and Defense on Graph Data: A Survey.** arxiv 2018. [paper](https://arxiv.org/pdf/1812.10528.pdf)
*Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li.*
1. **Deep Learning on Graphs: A Survey.** arxiv 2018. [paper](https://arxiv.org/pdf/1812.04202.pdf)
*Ziwei Zhang, Peng Cui, Wenwu Zhu.*
1. **Relational Inductive Biases, Deep Learning, and Graph Networks.** arxiv 2018. [paper](https://arxiv.org/pdf/1806.01261.pdf)
*Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others.*
1. **Geometric Deep Learning: Going beyond Euclidean data.** IEEE SPM 2017. [paper](https://arxiv.org/pdf/1611.08097.pdf)
*Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre.*
1. **Computational Capabilities of Graph Neural Networks.** IEEE TNN 2009. [paper](Excerpt of 100,713 characters
Read on GitHub
Tsinghua University
101
Ganqu CUI · Tsinghua University · China
40
Yushi Bai · Tsinghua University
7
Zhengyan Zhang
5
Guohao Li · CAMEL-AI.org
4
Zhiyuan Liu · Tsinghua University · China
4
2
Lukas Galke Poech · University of Southern Denmark · Denmark
2
2
Jinheon Baek
2
1
Kexin Huang · United States
1
1
1
1
1
1
1
1
Tzu-Heng Lin
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:153ec8e63a6ae1fa, llm:Repository topics: 'gnn', 'paper-list'; description: 'Must-read papers on graph neural networks (GNN)'; README lists surveys, models, applications and many GNN paper categories.
matched fp:153ec8e63a6ae1fa, llm:Repository topics: 'gnn', 'paper-list'; description: 'Must-read papers on graph neural networks (GNN)'; README lists surveys, models, applications and many GNN paper categories.
matched fp:153ec8e63a6ae1fa, llm:Repository topics: 'gnn', 'paper-list'; description: 'Must-read papers on graph neural networks (GNN)'; README lists surveys, models, applications and many GNN paper categories.
matched fp:153ec8e63a6ae1fa, llm:Repository topics: 'gnn', 'paper-list'; description: 'Must-read papers on graph neural networks (GNN)'; README lists surveys, models, applications and many GNN paper categories.