DGL (Deep Graph Library) and PyG (PyTorch Geometric) are libraries for deep learning on graphs. They provide useful abstractions for building and training graph models like GNNs using graph-structured data. However, the libraries differ in a few ways.
Backend:
- DGL: support for PyTorch and Tensorflow with greater flexibility
- PyG: support mostly for PyTorch
Graph Representation:
- DGL: support for flexible graph representations from heterogenous, multi-graphs, and more complex graph structures
- PyG: mostly focused on homogenous graphs with limited heterogeneity
API:
- DGL: support for low-level customizations with more control on graph computation
- PyG: support for higher-level and more user-friendly for common GNN architectures
Performance:
- DGL: better performance for large-scale graph datasets
- PyG: slower than DGL but performance has been improving
Community and Ecosystem:
- DGL: strong community with tutorials, examples, and third-party libraries
- PyG: large community with better ecosystem of tools and resources
When to use:
- DGL:
- When you need support for multiple backends
- When you require flexibility across a wide and complex graph structures
- When you prioritize performance and scalability
- When you need fine-grained control over graph computations
- PyG:
- When you are primarily focused on PyTorch
- When you are prototyping and need easy to use API
- When you mostly work in homogenous graphs
