GraphLearn r0.4.0 provided graph operating API and simple EgoGraph based GNN models. Recently we found that more and more users started to have the need for custom algorithms. In order to simplify the development of GNN algorithms, we have developed an algorithm framework for algorithm developers. This version supports both TF1.12 and PyTorch, and is also compatible with PyG. This GNN programming framework provides support for both fixed-size neighbor sampling and full neighbor sampling, and provides complete examples and algorithm development documentation.
New Features
- Refine Graph Sampling Language(GSL).
- Refactor the model implementation to simplify the model development process, abstract the data layer and model layer, and provide complete algorithm development examples and documentation.
- Refine EgoGraph based models.
- Add a new model development paradigm based on SubGraph.
- Add support for OGB data.
- Add a link prediction SEAL algorithm example.
- Add RGCN example.
- Add support for PyTorch.
- Add support for PyG.
Documentation
- https://graph-learn.readthedocs.io/en/latest/
r0.4.0