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Contributing to KHGNN

Thank you for your interest in contributing to KHGNN! This guide will help you get started.

🔧 Development Setup

  1. Fork and Clone

    git clone https://github.com/your-username/KHGNN.git
cd KHGNN

  2. Install Development Dependencies

    uv sync --group dev

  3. Install Pre-commit Hooks (Optional but recommended)

    uv run pre-commit install

📝 Code Style

We use automated code formatting tools to maintain consistency:

Automatic Formatting

  • Black: Code formatting
  • isort: Import sorting
  • Files are automatically formatted on save in VS Code

Manual Formatting

# Format all files
uv run black .

# Format specific files
uv run black filename.py
uv run isort filename.py

Code Standards

  • Follow PEP 8 guidelines
  • Use type hints where possible
  • Write docstrings for all public functions and classes
  • Keep line length to 88 characters (Black's default)

🧪 Testing

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_model.py

# Run with coverage
uv run pytest --cov=khgnn

📊 Experiments

When adding new features or modifications:

  1. Run baseline experiments to ensure reproducibility
  2. Document performance changes in your PR
  3. Update configs if adding new parameters
# Run quick validation
uv run example.py

# Run full experiments
uv run trans_multi_train.py
uv run prod_multi_exp.py

🚀 Pull Request Process

  1. Create a feature branch

    git checkout -b feature/your-feature-name

  2. Make your changes

    • Write clear, concise commit messages
    • Include tests for new features
    • Update documentation as needed

  3. Ensure quality

    # Run tests
uv run pytest

# Check for issues
uv run black --check .
uv run isort --check-only .

  4. Update documentation

    • Update README if adding new features
    • Add docstrings to new functions
    • Update example scripts if needed

  5. Submit PR

    • Provide a clear description of changes
    • Reference any related issues
    • Include before/after performance comparisons if applicable

🐛 Bug Reports

When reporting bugs, please include:

  • Environment details: Python version, PyTorch version, OS
  • Reproduction steps: Minimal code to reproduce the issue
  • Expected vs actual behavior
  • Error messages: Full stack traces if applicable

💡 Feature Requests

For new features:

  • Check existing issues to avoid duplicates
  • Provide motivation: Why is this feature needed?
  • Describe the interface: How should it work?
  • Consider backwards compatibility

📚 Documentation

We welcome improvements to documentation:

  • Code comments: Explain complex algorithms
  • Docstrings: Document all public APIs
  • README updates: Keep examples current
  • Tutorials: Help new users get started

🎯 Areas for Contribution

Current areas where contributions are especially welcome:

Core Features

  • Additional kernel types
  • Memory optimization
  • Multi-GPU support
  • Mixed precision training

Experiments

  • New benchmark datasets
  • Hyperparameter optimization
  • Ablation studies
  • Comparison with other methods

Infrastructure

  • Continuous integration
  • Performance benchmarks
  • Docker containers
  • Documentation improvements

🤝 Community Guidelines

  • Be respectful and constructive in discussions
  • Help others learn and contribute
  • Follow the code of conduct
  • Share knowledge through issues and discussions

📞 Getting Help

  • Open an issue for bugs or feature requests
  • Start a discussion for questions or ideas
  • Check existing issues before creating new ones

🏆 Recognition

Contributors will be acknowledged in:

  • README contributor section
  • Release notes for significant contributions
  • Academic papers (for algorithmic contributions)

Thank you for helping make KHGNN better! 🎉