PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. Click here to join our Slack community!
- Introduction by Example
- Creating Message Passing Networks
- Creating Your Own Datasets
- Heterogeneous Graph Learning
- Loading Graphs from CSV
- Managing Experiments with GraphGym
- Advanced Mini-Batching
- Memory-Efficient Aggregations
- TorchScript Support
- Scaling Up GNNs via Remote Backends
- GNN Cheatsheet
- Dataset Cheatsheet
- Colab Notebooks and Video Tutorials
- External Resources