STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization

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📋 Table of Contents

• ✨ Overview
• 🏗️ Repository Structure
• 🚀 Getting Started
• 🙏 Acknowledgements
• 📝 Citation
• 🌟 Star History

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✨ Overview

Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool for modeling dynamic graph-structured data across diverse domains. However, they often fail to generalize in Spatio-Temporal Out-of-Distribution (STOOD) scenarios, where both temporal dynamics and spatial structures evolve beyond the training distribution. To address this problem, we propose STRAP, which enhances model generalization by integrating retrieval-augmented learning into the STGNN continue learning pipeline. Extensive experiments across multiple real-world streaming graph datasets show that \methodname consistently outperforms state-of-the-art STGNN baselines on STOOD tasks, demonstrating its robustness, adaptability, and strong generalization capability without task-specific fine-tuning.

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🏗️ Repository Structure

STRAP/
│
├── 📄 README.md                    # Project documentation
├── 📄 LICENSE                      # Apache 2.0 License
├── 📄 environment.yaml             # Conda environment configuration
├── 🚀 main.py                      # Main entry point for experiments
├── 🚀 stkec_main.py               # STKEC experiments entry point
├── 📜 run.sh                       # Batch experiment execution script
│
├── 📁 conf/                        # ⚙️ Configuration files
│   ├── AIR/                       # Air quality dataset configs
│   ├── ENERGY-Wind/               # Wind energy dataset configs
│   └── PEMS/                      # Traffic dataset configs
│       ├── strap.json
│       ├── ewc.json
│       └── ...
│
├── 📁 src/                         # 💻 Source code
│   ├── dataer/                    # Data loading and preprocessing
│   │   ├── ...
│   │
│   ├── model/                     # Model implementations
│   │   ├── ...             # Model components
│   │
│   └── trainer/                   # Training and evaluation
│       ├── ...
│
├── 📁 utils/                       # 🛠️ Utility functions
│   ├── ...
│
├── 📁 font/                        # Font files for visualization
├── 📁 log/                         # 📊 Training logs and checkpoints
└── 📁 data/                        # 💾 Dataset storage (create this)

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🚀 Getting Started

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Conda or Miniconda (Download)
• NVIDIA GPU with CUDA support (recommended)
• Python 3.8+

💻 Usage

# ENERGY-Wind, the same for other datasets.
bash run.sh

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🙏 Acknowledgements

We would like to express our gratitude to:

• EAC: We thank the authors for their excellent work. Our implementation builds upon their codebase: EAC Repository

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📝 Citation

If you find this work useful for your research, please consider citing our paper:

@article{zhang2025strap,
  title={STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization},
  author={Zhang, Haoyu and Zhang, Wentao and Miao, Hao and Jiang, Xinke and Fang, Yuchen and Zhang, Yifan},
  journal={arXiv preprint arXiv:2505.19547},
  year={2025}
}

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🌟 Star History

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