External Resources

  • Matthias Fey and Jan E. Lenssen: Fast Graph Representation Learning with PyTorch Geometric [Paper, Slides (3.3MB), Poster (2.3MB), Notebook]

  • Soumith Chintala: Automatic Differentiation, PyTorch and Graph Neural Networks [Talk (starting from 26:15)]

  • Steeve Huang: Hands-on Graph Neural Networks with PyTorch & PyTorch Geometric [Tutorial, Code]

  • Stanford University: Graph Neural Networks using PyTorch Geometric [Talk (starting from 33:33)]

  • Nicolas Chaulet et al.: PyTorch Points 3D - A framework for running common deep learning models for point cloud analysis tasks that heavily relies on Pytorch Geometric [Github, Documentation]

  • Weihua Hu et al.: Open Graph Benchmark - A collection of large-scale benchmark datasets, data loaders, and evaluators for graph machine learning, including PyTorch Geometric support and examples [Website, GitHub]

  • DeepSNAP - A PyTorch library that bridges between graph libraries such as NetworkX and PyTorch Geometric [GitHub, Documentation]

  • Benedek Rozemberczki: PyTorch Geometric Temporal - A temporal GNN library built upon PyTorch Geometric [GitHub, Documentation]

  • Amitoz Azad: torch_pdegraph - Solving PDEs on graphs with PyTorch Geometric [Devpost, GitHub]