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GPM represents a significant step towards next-generation graph learning backbone by moving beyond traditional message passing approaches.\n\n### Key Features\n- 🔍 Direct learning from graph substructures instead of message passing\n- 🚀 Enhanced ability to capture long-range dependencies\n- 💡 Efficient extraction and encoding of task-relevant graph patterns\n- 🎯 Superior expressivity in handling complex graph structures\n\n### Framework Overview\n\u003cimg src=\"assets/workflow.png\"\u003e\n\u003cimg src=\"assets/framework.png\"\u003e\n\nGPM's workflow consists of three main steps:\n1. Pattern extraction using random walk tokenizer\n2. Pattern encoding via sequential modeling\n3. Pattern processing through transformer encoder for downstream tasks\n\n## 🛠️ Installation\n\n### Prerequisites\n- CUDA-compatible GPU (24GB memory minimum, 48GB recommended)\n- CUDA 12.1\n- Python 3.9+\n\n### Setup\n```bash\n# Create and activate conda environment\nconda env create -f environment.yml\nconda activate GPM\n\n# Install DGL\npip install dgl -f https://data.dgl.ai/wheels/torch-2.4/cu121/repo.html\n\n# Install PyG dependencies\npip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.4.0+cu121.html\n```\n\n## 🚀 Quick Start\nThe code of GPM is presented in folder `/GPM`. You can run `main.py` and specify any dataset to run experiments. To ensure reproducability, we provide hyper-parameters in `config/main.yaml`. You can simply use command `--use_params` to set tuned hyper-parameters. \n\n### Basic Usage\n```bash\n# Run with default parameters\npython GPM/main.py --dataset computers --use_params\n```\n\n### Supported Tasks \u0026 Datasets\n\n1. **Node Classification**\n   - `cora_full`, `computers`, `arxiv`, `products`\n   - `wikics`, `deezer`, `blog`, `flickr`, `flickr_small`\n\n2. **Link Prediction**\n   - `link-cora`, `link-pubmed`, `link-collab`\n\n3. **Graph Classification**\n   - `imdb-b`, `collab`, `reddit-m5k`, `reddit-m12k`\n\n4. **Graph Regression**\n   - `zinc`, `zinc_full`\n\nWe also provide the interfaces of other widely used datasets like \n`photo`, `physics`, `reddit`, etc. Please check the datasets in `GPM/data/pyg_data_loader.py` for details. \n\n\n\n## 🔧 Configuration Options\n\n### Training Parameters\n- `--use_params`: Use tuned hyperparameters\n- `--dataset`: Target dataset name\n- `--epochs`: Number of training epochs\n- `--batch_size`: Batch size\n- `--lr`: Learning rate\n- `--split`: Data split strategy (`public`, `low`, `median`, `high`)\n\n### Model Architecture\n- `--hidden_dim`: Hidden layer dimension\n- `--heads`: Number of attention heads\n- `--num_layers`: Number of Transformer layers\n- `--dropout`: Dropout rate\n\n### Pattern Configuration\n- `--num_patterns`: Number of patterns per instance\n- `--pattern_size`: Pattern size (random walk length)\n- `--multiscale`: Range of walk lengths\n- `--pattern_encoder`: Pattern encoder type (`transformer`, `mean`, `gru`)\n\nFor complete configuration options, please refer to our code documentation.\n\n## 🔄 Domain Adaptation\n\nRun domain adaptation experiments using:\n```bash\npython GPM/da.py --source acm --target dblp --use_params\n```\n\nSupported domain pairs:\n- `acm -\u003e dblp`, `dblp -\u003e acm`\n- `DE -\u003e {EN, ES, FR, PT, RU}`\n\n## 📂 Repository Structure\n```\n└── GPM\n    ├── GPM/              # Main package directory\n    │   ├── data/         # Data loading and preprocessing\n    │   ├── model/        # Model architectures\n    │   ├── task/         # Task implementations\n    │   ├── utils/        # Utility functions\n    │   ├── main.py       # Main training script\n    │   └── da.py         # Domain adaptation script\n    ├── config/           # Configuration files\n    ├── assets/           # Images and assets\n    ├── data/             # Dataset storage\n    ├── patterns/         # Extracted graph patterns\n    └── environment.yml   # Conda environment spec\n```\n\n## 📚 Citation\n\nIf you find this work useful, please cite our paper:\n\n```bibtex\n@inproceedings{wang2025gpm,\n  title={Beyond Message Passing: Neural Graph Pattern Machine},\n  author={Wang, Zehong and Zhang, Zheyuan and Ma, Tianyi and Chawla, Nitesh V and Zhang, Chuxu and Ye, Yanfang},\n  booktitle={Forty-Second International Conference on Machine Learning},\n  year={2025}, \n}\n\n@article{wang2025neural,\n  title={Neural Graph Pattern Machine},\n  author={Wang, Zehong and Zhang, Zheyuan and Ma, Tianyi and Chawla, Nitesh V and Zhang, Chuxu and Ye, Yanfang},\n  journal={arXiv preprint arXiv:2501.18739},\n  year={2025}\n}\n```\n\n## 👥 Authors\n\n- [Zehong Wang](https://zehong-wang.github.io/)\n- [Zheyuan Zhang](https://jasonzhangzy1757.github.io/)\n- [Tianyi Ma](https://tianyi-billy-ma.github.io/)\n- [Nitesh V Chawla](https://niteshchawla.nd.edu/)\n- [Chuxu Zhang](https://chuxuzhang.github.io/)\n- [Yanfang Ye](http://yes-lab.org/)\n\nFor questions, please contact `zwang43@nd.edu` or open an issue.\n\n## 🙏 Acknowledgements\n\nThis repository builds upon the excellent work from:\n- [PyG](https://github.com/pyg-team/pytorch_geometric)\n- [OGB](https://github.com/snap-stanford/ogb)\n- [VQ](https://github.com/lucidrains/vector-quantize-pytorch)\n\nWe thank these projects for their valuable contributions to the field.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzehong-wang%2Fgpm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzehong-wang%2Fgpm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzehong-wang%2Fgpm/lists"}