GFMPapers

<h2 align="center"><b>GFMPapers: Must-read papers on graph foundation models (GFMs)</b></h2>

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This list is currently maintained by members in BUPT GAMMA Lab. If you like our project, please give us a star ⭐ on GitHub for the latest update.

We thank all the great contributors very much.

⭐ We have held a tutorial about graph foundation model at the WebConf 2024! Here is the tutorial. [part1][part2][part3]

Contents

• Contents
• Keywords Convention
• 0. Survey Papers
• 1. GNN-based Papers
• 2. LLM-based Papers
• 3. GNN+LLM-based Papers
• 4. Benchmarks and Datasets
• 5. Other papers
• Contributors

Keywords Convention

backbone architecture

Pretraining

Adaptation

The meaning of each tag can be referred to in the "Towards Graph Foundation Models: A Survey and Beyond" paper.

0. Survey Papers

1. [TPAMI 2025] Graph Foundation Models: Concepts, Opportunities and Challenges. [pdf]
2. [TKDE 2024] Large Language Models on Graphs: A Comprehensive Survey. [pdf][paperlist]
3. [KDD 2025] Graph Foundation Models: Challenges, Methods, and Open Questions. [pdf]
4. [KDD 2024] A Survey of Large Language Models for Graphs. [pdf]
5. [KDD 2024] Graph Intelligence with Large Language Models and Prompt Learning. [pdf]
6. [arXiv 2023.8] Graph Meets LLMs: Towards Large Graph Models. [pdf][paperlist]
7. [IEEE Intelligent Systems] Integrating Graphs with Large Language Models: Methods and Prospects. [pdf]
8. [arXiv 2023.10] Towards Graph Foundation Models: A Survey and Beyond. [pdf][paperlist]
9. [arXiv 2023.11] A Survey of Graph Meets Large Language Model: Progress and Future Directions. [pdf][paperlist]
10. [arXiv 2024.2] Graph Foundation Models. [pdf][paperlist][paperlist2]
11. [arXiv 2024.2] Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques. [pdf]
12. [arXiv 2024.2] Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models. [pdf]
13. [arXiv 2024.3] A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective. [pdf][paperlist]
14. [arXiv 2024.4] A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications. [pdf]
15. [TIST 2024] Graph Machine Learning in the Era of Large Language Models (LLMs). [pdf]
16. [arXiv 2024.12] Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks. [pdf]
17. [arXiv 2025.3] Towards Graph Foundation Models: A Transferability Perspective. [pdf]
18. [arXiv 2025.5] Graph Foundation Models: A Comprehensive Survey. [pdf]
19. [arXiv 2025.5] Using Large Language Models to Tackle Fundamental Challenges in Graph Learning: A Comprehensive Survey. [pdf]
20. [arXiv 2025.6] Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities. [pdf][paperlist]

1. GNN-based Papers

1. [arXiv 2023.10] Enhancing Graph Neural Networks with Structure-Based Prompt [pdf]
2. [arXiv 2023.11] MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs [pdf]
3. [arXiv 2023.10] HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks [pdf]
4. [arXiv 2023.10] Prompt Tuning for Multi-View Graph Contrastive Learning [pdf]
5. [arXiv 2023.05] PRODIGY: Enabling In-context Learning Over Graphs. [pdf]
6. [arXiv 2023.05] G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks. [pdf]
7. [arXiv 2023.04] AdapterGNN: Efficient Delta Tuning Improves Generalization Ability in Graph Neural Networks. [pdf]
8. [arXiv 2023.02] SGL-PT: A Strong Graph Learner with Graph Prompt Tuning. [pdf]
9. [KDD 2023] All in One: Multi-Task Prompting for Graph Neural Networks. [pdf]
10. [KDD 2023] A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability. [pdf] [code]
11. [AAAI 2023] Ma-gcl: Model augmentation tricks for graph contrastive learning. [pdf] [code]
12. [WWW 2023] GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. [pdf] [code]
13. [WWW 2023] Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. [pdf] [code]
14. [CIKM 2023] Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks. [pdf] [code]
15. [KDD 2022] GraphMAE: Self-supervised masked graph autoencoders. [pdf] [code]
16. [KDD 2022] **Gppt: Graph