相关资料
==================
 
* Matthias Fey and Jan E. Lenssen: **Fast Graph Representation Learning with PyTorch Geometric** [`Paper <https://arxiv.org/abs/1903.02428>`_, `Slides (3.3MB) <http://rusty1s.github.io/pyg_slides.pdf>`__, `Poster (2.3MB) <http://rusty1s.github.io/pyg_poster.pdf>`__, `Notebook <http://htmlpreview.github.io/?https://github.com/rusty1s/rusty1s.github.io/blob/master/pyg_notebook.html>`__]

* Soumith Chintala: **Automatic Differentiation, PyTorch and Graph Neural Networks** [`Talk (starting from 26:15) <http://www.ipam.ucla.edu/abstract/?tid=15592&pcode=GLWS4>`__]

* Steeve Huang: **Hands-on Graph Neural Networks with PyTorch & PyTorch Geometric** [`Tutorial <https://towardsdatascience.com/hands-on-graph-neural-networks-with-pytorch-pytorch-geometric-359487e221a8>`__, `Code <https://github.com/khuangaf/Pytorch-Geometric-YooChoose>`__]

* Nicolas Chaulet *et al.*: **PyTorch Point Cloud Benchmark** - A framework for running common deep learning models for point cloud analysis tasks that heavily relies on Pytorch Geometric [`Github <https://github.com/nicolas-chaulet/deeppointcloud-benchmarks>`__, `Documentation <https://deeppointcloud-benchmarks.readthedocs.io/en/latest/>`__]

* 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 <https://ogb.stanford.edu>`__, `GitHub <https://github.com/snap-stanford/ogb>`__]