Awesome-Graph-Prompt
A collection of AWESOME things about performing prompt learning on Graphs.
 
Recently, the workflow of **"pre-train, fine-tune"** has been shown less effective and efficient when dealing with diverse downstream tasks on graph domain.
Inspired by the prompt learning in natural language processing (NLP) domain, the **"pre-train, prompt"** workflow has emerged as a promising solution.
This repo aims to provide a curated list of research papers that explore the prompt learning on graphs.
**It is based on our Survey Paper: [Graph Prompt Learning: A Comprehensive Survey and Beyond](https://arxiv.org/abs/2311.16534)**.
We will try to make this list updated frequently. If you found any error or any missed paper, please don't hesitate to open issues or pull requests.🌹
## Table of Contents
- [Awesome-Graph-Prompt](#awesome-graph-prompt)
- [Table of Contents](#table-of-contents)
- [GNN Prompting Papers](#gnn-prompting-papers)
- [Multi-Modal Prompting with Graphs](#multi-modal-prompting-with-graphs)
- [Prompt in Text-Attributed Graphs](#prompt-in-text-attributed-graphs)
- [Large Language Models in Graph Data Processing](#large-language-models-in-graph-data-processing)
- [Multi-modal Fusion with Graph and Prompting](#multi-modal-fusion-with-graph-and-prompting)
- [Graph Domain Adaptation with Prompting](#graph-domain-adaptation-with-prompting)
- [Application Papers](#application-papers)
- [Dynamic Graphs](#dynamic-graphs)
- [Social Networks](#social-networks)
- [Recommender Systems](#recommender-systems)
- [Knowledge Graph](#knowledge-graph)
- [Biology](#biology)
- [Others](#others)
- [Other Resources](#other-resources)
- [Open Source](#open-source)
- [Benchmarks](#benchmarks)
- [Datasets](#datasets)
- [Online Talks](#online-talks)
- [Blogs](#blogs)
- [Contributing](#contributing)
- [Citation](#citation)
## GNN Prompting Papers
Summary of existing representative works on graph prompt. $\mathcal{S}$: Subgraph. $V(\mathcal{S})$: Node set within subgraph $\mathcal{S}$. $\pi$: Pre-trained parameters. $\phi$: Task head parameters. $\theta$: Prompt parameters. $\widetilde{\mathbf{s}}$: Filled prompt.
1. **Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis.**
In **arXiv**, [[Paper](https://arxiv.org/abs/2410.01635)].
   
2. **GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks**.
In **KDD'2022**, [[Paper](https://dl.acm.org/doi/10.1145/3534678.3539249 )] [[Code](https://github.com/MingChen-Sun/GPPT)].
  
3. **SGL-PT: A Strong Graph Learner with Graph Prompt Tuning**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2302.12449)].
  
4. **GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks**.
In **WWW'2023**, [[Paper](https://dl.acm.org/doi/10.1145/3543507.3583386 )] [[Code](https://github.com/Starlien95/GraphPrompt )].
  
5. **All in One: Multi-Task Prompting for Graph Neural Networks**.
In **KDD'2023** Best Paper Award 🌟, [[Paper](https://arxiv.org/abs/2307.01504 )] [[Code](https://github.com/sheldonresearch/ProG)].
  
6. **Deep Graph Reprogramming**.
In **CVPR'2023** Highlight 🌟, [[Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.pdf)].
  
7. **Virtual Node Tuning for Few-shot Node Classification**.
In **KDD'2023**, [[Paper](https://arxiv.org/abs/2306.06063)].
  
8. **PRODIGY: Enabling In-context Learning Over Graphs**.
In **NeurIPS'2023** Spotlight 🌟, [[Paper](https://arxiv.org/abs/2305.12600)] [[Code](https://github.com/snap-stanford/prodigy )].
  
9. **Universal Prompt Tuning for Graph Neural Networks**.
In **NeurIPS'2023**, [[Paper](https://arxiv.org/abs/2209.15240)] [[Code](https://github.com/LuckyTiger123/GPF)].
  
10. **Deep Prompt Tuning for Graph Transformers**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2309.10131)].
  
11. **Prompt Tuning for Multi-View Graph Contrastive Learning**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2310.10362)].
  
12. **ULTRA-DP:Unifying Graph Pre-training with Multi-task Graph Dual Prompt**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2310.14845)].
  
13. **HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks**.
In **WWW'2024**, [[Paper](https://arxiv.org/abs/2310.15318)].
  
14. **Enhancing Graph Neural Networks with Structure-Based Prompt**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2310.17394)].
  
15. **Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs**.
In **TKDE'2024**, [[Paper](https://arxiv.org/abs/2311.15317)] [[Code](https://github.com/gmcmt/graph_prompt_extension)].
  
16. **HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning**.
In **AAAI'2024**, [[Paper](https://arxiv.org/abs/2312.01878)] [[Code](https://github.com/Starlien95/HGPrompt)].
  
17. **MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs**.
In **WWW'2024**, [[Paper](https://arxiv.org/abs/2312.03731)] [[Code](https://github.com/Nashchou/MultiGPrompt)].
  
18. **Subgraph-level Universal Prompt Tuning**.
In **arXiv**, [[Paper](https://arxiv.org/pdf/2402.10380.pdf)] [[Code](https://anonymous.4open.science/r/SUPT-F7B1/)].
  
19. **Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective**.
In **WWW'2024**, [[Paper](https://arxiv.org/pdf/2402.13556.pdf)].
  
20. **A Unified Graph Selective Prompt Learning for Graph Neural Networks**.
In **arXiv**, [[Paper](https://arxiv.org/pdf/2406.10498)].
  
## Multi-Modal Prompting with Graphs
### Prompt in Text-Attributed Graphs
1. **Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting**.
In **SIGIR'2023**, [[Paper](https://arxiv.org/abs/2305.03324 )] [[Code](https://github.com/WenZhihao666/G2P2 )].
2. **Prompt Tuning on Graph-augmented Low-resource Text Classification**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2307.10230 )] [[Code](https://github.com/WenZhihao666/G2P2-conditional )].
3. **Prompt-Based Zero- and Few-Shot Node Classification: A Multimodal Approach**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2307.11572 )].
4. **Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2309.02848 )].
5. **Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs**.
In **arXiv**, [[Paper](https://arxiv.org/pdf/2311.14324.pdf )].
6. **ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs**.
In **KDD'2024**, [[Paper](https://arxiv.org/abs/2402.11235)] [[Code](https://github.com/NineAbyss/ZeroG)].
7. **Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs**.
In **KDD'2024**, [[Paper](https://arxiv.org/abs/2407.15431)] [[Code](https://github.com/THUDM/P2TAG)].
### Large Language Models in Graph Data Processing
>
> For this research line, please refer to **Awesome LLMs with Graph Tasks** [[Survey Paper](https://arxiv.org/abs/2311.12399) | [Github Repo](https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks)]
>
> We **highly recommend** this work as they have provided a comprehensive survey to summarize the works on the integration of **LLM and Graph** 👍
### Multi-modal Fusion with Graph and Prompting
1. **GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph**.
In **NeurIPS'2023**, [[Paper](http://arxiv.org/abs/2309.13625)] [[Code](https://github.com/lixinustc/GraphAdapter )]. `Graph+Text+Image`
2. **SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design**.
In **arXiv**, [[Paper](http://arxiv.org/abs/2307.11694)]. `Graph+Text`
3. **Which Modality should I use - Text, Motif, or Image? Understanding Graphs with Large Language Models**.
In **arXiv**, [[Paper](https://arxiv.org/pdf/2311.09862.pdf)]. `Graph+Text+Image`
## Graph Domain Adaptation with Prompting
1. **GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks**.
In **KDD'2023**, [[Paper](https://arxiv.org/pdf/2306.11264.pdf)] [[Code](https://github.com/WtaoZhao/GraphGLOW )].
2. **GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning**.
In **WWW'2024**, [[Paper](https://arxiv.org/abs/2310.07365)] [[Code](https://github.com/wykk00/GraphControl)] [[Chinese Blog](https://zhuanlan.zhihu.com/p/680351601)].
 
3. **All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining**.
In **KDD'2024**, [[Paper](https://arxiv.org/abs/2402.09834)] [[Code](https://github.com/cshhzhao/GCOPE)].
4. **Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2405.13934)].
## Application Papers
### Dynamic Graphs
1. **Prompt Learning on Temporal Interaction Graphs**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2402.06326)].
2. **Prompt-Enhanced Spatio-Temporal Graph Transfer Learning**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2405.12452)].
3. **DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2405.13937)].
### Social Networks
1. **Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection**.
In **CIKM'2023**, [[Paper](https://arxiv.org/pdf/2309.16424.pdf )] [[Code](https://github.com/jiayingwu19/Prompt-and-Align)]. `Fake News Detection`
2. **Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks**.
In **CIKM'2023**, [[Paper](https://arxiv.org/abs/2308.10028 )]. `Fraud Detection`
3. **ProCom: A Few-shot Targeted Community Detection Algorithm**.
In **KDD'2024**, [[Paper](https://arxiv.org/abs/2408.07369)] [[Code](https://github.com/WxxShirley/KDD2024ProCom)]. `Community Detection`
### Recommender Systems
1. **Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations**.
In **arXiv 2024**, [[Paper](https://arxiv.org/abs/2410.11719)]. `Cross-domain Recommendation`
2. **Contrastive Graph Prompt-tuning for Cross-domain Recommendation**.
In **TOIS'2023**, [[Paper](https://arxiv.org/pdf/2308.10685.pdf )]. `Cross-domain Recommendation`
3. **An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations**.
In **NeurIPS'2023**, [[Paper](https://openreview.net/pdf?id=XyAP8ScqLV)] [[Code](https://github.com/Haoran-Young/CPTPP )]. `General Recommendation`
4. **Motif-Based Prompt Learning for Universal Cross-Domain Recommendation**.
In **WSDM'2024**, [[Paper](https://arxiv.org/abs/2310.13303)]. `Cross-domain Recommendation`
5. **GraphPro: Graph Pre-training and Prompt Learning for Recommendation**.
In **WWW'2024**, [[Paper](https://arxiv.org/abs/2311.16716)] [[Code](https://github.com/HKUDS/GraphPro)]. `General Recommendation`
6. **GPT4Rec: Graph Prompt Tuning for Streaming Recommendation**.
In **SIGIR'2024**, [[Paper](https://arxiv.org/pdf/2406.08229)]. `General Recommendation`
### Knowledge Graph
1. **Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer**.
In **WWW'2023**, [[Paper](https://arxiv.org/pdf/2303.03922.pdf )] [[Code](https://github.com/zjukg/KGTransformer )].
2. **Graph Neural Prompting with Large Language Models**.
In **AAAI'2024**, [[Paper](https://arxiv.org/pdf/2309.15427.pdf)].
3. **Knowledge Graph Prompting for Multi-Document Question Answering**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2308.11730 )] [[Code](https://github.com/YuWVandy/KG-LLM-MDQA )].
4. **Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2405.13085)] [[Code](https://github.com/zjukg/MuDoK)].
### Biology
1. **Can Large Language Models Empower Molecular Property Prediction?**
In **arXiv**, [[Paper](https://arxiv.org/pdf/2307.07443.pdf)] [[Code](https://github.com/ChnQ/LLM4Mol)].
2. **GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning**.
In **NeurIPS'2023**, [[Paper](https://arxiv.org/pdf/2306.13089.pdf)] [[Code](https://github.com/zhao-ht/GIMLET )].
3. **MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter**.
In **EMNLP'2023**, [[Paper](http://arxiv.org/abs/2310.12798)] [[Code](https://github.com/acharkq/MolCA)].
4. **ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction**.
In **EMNLP'2023**, [[Paper](https://arxiv.org/pdf/2310.13590.pdf)] [[Code](https://github.com/syr-cn/ReLM)].
5. **MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning**.
In **WSDM'2024**, [[Paper](https://arxiv.org/abs/2212.10614)].
6. **Protein Multimer Structure Prediction via PPI-guided Prompt Learning**.
In **ICLR'2024**, [[Paper](https://openreview.net/forum?id=OHpvivXrQr)].
7. **DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning**.
In **CIKM'2024**, [[Paper](https://arxiv.org/abs/2402.11472)].
### Others
1. **A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability**.
In **KDD'2023**, [[Paper](https://dl.acm.org/doi/abs/10.1145/3580305.3599244)] [[Code](https://github.com/BUPT-GAMMA/AAGOD )]. `OOD Detection`
2. **MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2312.14574)].
3. **Instruction-based Hypergraph Pretraining**.
In **SIGIR'2024**, [[Paper](https://arxiv.org/abs/2403.19063)]. `Hypergraph Prompt`
4. **Cross-Context Backdoor Attacks against Graph Prompt Learning**.
In **KDD'2024**, [[Paper](https://arxiv.org/pdf/2405.17984)] [[Code](https://github.com/xtLyu/CrossBA)]. `Cross-Context Backdoor Attacks`
5. **Urban Region Pre-training and Prompting: A Graph-based Approach**.
In **arXiv**, [[Paper](https://arxiv.org/abs/2408.05920)]. `Urban Region Representation`
## Other Resources
### Open Source
* **ProG: A Unified Library for Graph Prompting** [[Website](https://graphprompt.github.io/)] [[Code](https://github.com/sheldonresearch/ProG)]
ProG (Prompt Graph) is a library built upon PyTorch to easily conduct single or multiple task prompting for a pre-trained Graph Neural Networks (GNNs).
### Benchmarks
* **ProG: A Graph Prompt Learning Benchmark** [[Paper](https://arxiv.org/pdf/2406.05346)]
ProG benchmark integrates **SIX** pre-training methods and **FIVE** state-of-the-art graph prompt techniques, evaluated across **FIFTEEN** diverse datasets to assess **performance, flexibility, and efficiency**.
### Datasets
Datasets that are commonly used in GNN prompting papers.
Citation Networks
| Dataset | \#Node | \#Edge | \#Feature | \#Class |
| :--------------: | :-----------: | :-----------: | :-------: | :-----: |
| Cora | 2708 | 5429 | 1433 | 7 |
| CoraFull | 19793 | 63421 | 8710 | 70 |
| Citeseer | 3327 | 4732 | 3703 | 6 |
| DBLP | 17716 | 105734 | 1639 | 4 |
| Pubmed | 19717 | 44338 | 500 | 3 |
| Coauthor-CS | 18333 | 81894 | 6805 | 15 |
| Coauthor-Physics | 34493 | 247962 | 8415 | 5 |
| ogbn-arxiv | 169343 | 1166243 | 128 | 40 |
Purchase Networks
| Dataset | \#Node | \#Edge | \#Feature | \#Class |
| :--------------: | :-----------: | :-----------: | :-------: | :-----: |
| Amazon-Computers | 13752 | 245861 | 767 | 10 |
| Amazon-Photo | 7650 | 119081 | 745 | 8 |
| ogbn-products | 2449029 | 61859140 | 100 | 47 |
Social Networks
| Dataset | \#Node | \#Edge | \#Feature | \#Class |
| :--------------: | :-----------: | :-----------: | :-------: | :-----: |
| Reddit | 232965 | 11606919 | 602 | 41 |
| Flickr | 89250 | 899756 | 500 | 7 |
Molecular Graphs
| Dataset | \#Graph | \#Node (Avg.) | \#Edge (Avg.) | \#Feature | \#Class |
| :--------------:| :-----: | :-----------: | :-----------: | :-------: | :-----: |
| COX2 | 467 | 41.22 | 43.45 | 3 | 2 |
| ENZYMES | 600 | 32.63 | 62.14 | 18 | 6 |
| MUTAG | 188 | 17.93 | 19.79 | 7 | 2 |
| MUV | 93087 | 24.23 | 26.28 | - | 17 |
| HIV | 41127 | 25.53 | 27.48 | - | 2 |
| SIDER | 1427 | 33.64 | 35.36 | - | 27 |
### Online Talks
* Official Presentation of **All in One** [Link](https://www.bilibili.com/video/BV1q94y1k7nF)
### Blogs
* A Chinese Blog that provides a comprehensive introduction of **ALL** graph prompting works [[Zhihu](https://zhuanlan.zhihu.com/p/681628720)]
## Contributing
👍 Contributions to this repository are welcome!
If you have come across relevant resources, feel free to open an issue or submit a pull request.
## Citation
If you find this repo helpful to you, please feel free to cite these works:
[Survey Paper](https://arxiv.org/abs/2311.16534)
```latex
@article{sun2023graph,
title = {Graph Prompt Learning: A Comprehensive Survey and Beyond},
author = {Sun, Xiangguo and Zhang, Jiawen and Wu, Xixi and Cheng, Hong and Xiong, Yun and Li, Jia},
year = {2023},
journal = {arXiv:2311.16534},
eprint = {2311.16534},
archiveprefix = {arxiv}
}
```
[Tutorial](https://dl.acm.org/doi/abs/10.1145/3637528.3671456)
```latex
@inproceedings{li2024graph,
title={Graph Intelligence with Large Language Models and Prompt Learning},
author={Li, Jia and Sun, Xiangguo and Li, Yuhan and Li, Zhixun and Cheng, Hong and Yu, Jeffrey Xu},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={6545--6554},
year={2024}
}
```
[ProG Library](https://github.com/sheldonresearch/ProG)
```latex
@inproceedings{sun2023all,
title={All in One: Multi-Task Prompting for Graph Neural Networks},
author={Sun, Xiangguo and Cheng, Hong and Li, Jia and Liu, Bo and Guan, Jihong},
booktitle={Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining (KDD'23)},
year={2023},
pages = {2120–2131},
location = {Long Beach, CA, USA},
isbn = {9798400701030},
url = {https://doi.org/10.1145/3580305.3599256},
doi = {10.1145/3580305.3599256}
}
```
[Theoretical Support](https://arxiv.org/abs/2410.01635)
```latex
@article{wang2024does,
title={Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis},
author={Qunzhong Wang and Xiangguo Sun and Hong Cheng},
year={2024},
journal = {arXiv preprint arXiv:2410.01635},
url={https://arxiv.org/abs/2410.01635}
}
```
Other Representative Works:
🔥 **All in One** A Representative GNN Prompting Framework
```latex
@inproceedings{sun2023all,
title={All in One: Multi-Task Prompting for Graph Neural Networks},
author={Sun, Xiangguo and Cheng, Hong and Li, Jia and Liu, Bo and Guan, Jihong},
booktitle={Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining (KDD'23)},
year={2023},
pages = {2120–2131},
location = {Long Beach, CA, USA},
isbn = {9798400701030},
url = {https://doi.org/10.1145/3580305.3599256},
doi = {10.1145/3580305.3599256}
}
```
🔥 **All in One and One for All** A Cross-domain Graph Pre-training Framework
```latex
@article{zhao2024all,
title={All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining},
author={Haihong Zhao and Aochuan Chen and Xiangguo Sun and Hong Cheng and Jia Li},
year={2024},
eprint={2402.09834},
archivePrefix={arXiv}
}
```
🔥 **TIGPrompt** A Temporal Interation Graph Prompting Framework
```latex
@article{chen2024prompt,
title={Prompt Learning on Temporal Interaction Graphs},
author={Xi Chen and Siwei Zhang and Yun Xiong and Xixi Wu and Jiawei Zhang and Xiangguo Sun and Yao Zhang and Yinglong Zhao and Yulin Kang},
year={2024},
eprint={2402.06326},
archivePrefix={arXiv},
journal = {arXiv:2402.06326}
}
```
🔥 **Graph Prompting Works on Biology Domain**
```latex
@inproceedings{gao2024protein,
title={Protein Multimer Structure Prediction via {PPI}-guided Prompt Learning},
author={Ziqi Gao and Xiangguo Sun and Zijing Liu and Yu Li and Hong Cheng and Jia Li},
booktitle={The Twelfth International Conference on Learning Representations (ICLR)},
year={2024},
url={https://openreview.net/forum?id=OHpvivXrQr}
}
@article{wang2024ddiprompt,
title={DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning},
author={Yingying Wang and Yun Xiong and Xixi Wu and Xiangguo Sun and Jiawei Zhang},
year={2024},
eprint={2402.11472},
archivePrefix={arXiv},
journal = {arXiv:2402.11472}
}
```
🔥 **Graph Prompting Works on Cross-domain Recommendation**
```latex
@article{zhang2024adaptive,
title={Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations},
author={Hengyu Zhang and Chunxu Shen and Xiangguo Sun and Jie Tan and Yu Rong and Chengzhi Piao and Hong Cheng and Lingling Yi},
journal={arXiv preprint arXiv:2410.11719},
year={2024}
}
```
🔥 **Graph Prompting Works on Urban Computing**
```latex
@article{jin2024urban,
title={Urban Region Pre-training and Prompting: A Graph-based Approach},
author={Jin, Jiahui and Song, Yifan and Kan, Dong and Zhu, Haojia and Sun, Xiangguo and Li, Zhicheng and Sun, Xigang and Zhang, Jinghui},
journal={arXiv preprint arXiv:2408.05920},
year={2024}
}
```