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External Resources

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External Resources

  • Fey et al.: PyG 2.0: Scalable Learning on Real World Graphs [Paper <https://arxiv.org/abs/2507.16991>__]

  • Matthias Fey and Jan E. Lenssen: Fast Graph Representation Learning with :pyg:null 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>__]

  • :stanford:Stanford CS224W: Machine Learning with Graphs: Graph Machine Learning lectures [:youtube:null Youtube <https://www.youtube.com/watch?v=JAB_plj2rbA>__]

  • :stanford:Stanford University: A collection of graph machine learning tutorial blog posts, fully realized with :pyg:null PyG [Website <https://medium.com/stanford-cs224w>__]

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

  • Stanford University: Graph Neural Networks using :pyg:null PyTorch Geometric [:youtube:null YouTube (starting from 33:33) <https://www.youtube.com/watch?v=-UjytpbqX4A&feature=youtu.be>__]

  • Antonio Longa, Gabriele Santin and Giovanni Pellegrini: :pyg:null PyTorch Geometric Tutorial [Website <https://antoniolonga.github.io/Pytorch_geometric_tutorials>, :github:null GitHub <https://github.com/AntonioLonga/PytorchGeometricTutorial>]

  • DAIR.AI | elvis: Introduction to GNNs with :pyg:null PyTorch Geometric [Website <https://github.com/dair-ai/GNNs-Recipe>, :colab:null Colab <https://colab.research.google.com/drive/1d0jLDwgNBtjBVQOFe8lO_1WrqTVeVZx9?usp=sharing>]

  • 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:null GitHub <https://github.com/nicolas-chaulet/torch-points3d>, Documentation <https://torch-points3d.readthedocs.io/en/latest/>]

  • Weihua Hu et al.: :ogb:null Open Graph Benchmark - A collection of large-scale benchmark datasets, data loaders, and evaluators for graph machine learning, including :pyg:PyG support and examples [Website <https://ogb.stanford.edu>, :github:null GitHub <https://github.com/snap-stanford/ogb>]

  • DeepSNAP - A :pytorch:PyTorch library that bridges between graph libraries such as NetworkX and :pyg:PyG [:github:null GitHub <https://github.com/snap-stanford/deepsnap>, Documentation <https://snap.stanford.edu/deepsnap/>]

  • Quiver - A distributed graph learning library for :pyg:PyG [:github:null GitHub <https://github.com/quiver-team/torch-quiver>__]

  • Benedek Rozemberczki: PyTorch Geometric Temporal - A temporal GNN library built upon :pyg:PyG [:github:null GitHub <https://github.com/benedekrozemberczki/pytorch_geometric_temporal>, Documentation <https://pytorch-geometric-temporal.readthedocs.io/en/latest/>]

  • Yixuan He: PyTorch Geometric Signed Directed - A signed and directed GNN library built upon :pyg:PyG [:github:null GitHub <https://github.com/SherylHYX/pytorch_geometric_signed_directed>, Documentation <https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/>]

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

  • Francesco Landolfi: :pyg:null PyTorch Geometric Tutorial [PDF (0.4MB) <http://pages.di.unipi.it/citraro/files/slides/Landolfi_tutorial.pdf>__]

  • Sachin Sharma: How to Deploy (almost) any :pyg:null PyTorch Geometric Model on Nvidia's Triton Inference Server with an Application to Amazon Product Recommendation and ArangoDB [Blog <https://sachinsharma9780.medium.com/how-to-deploy-almost-any-pytorch-geometric-model-on-nvidias-triton-inference-server-with-an-218d0c0c679c>__]

  • Amitoz Azad: torch_pdegraph - Solving PDEs on Graphs with :pyg:PyG [Devpost <https://devpost.com/software/gdfgddfd>, :github:null GitHub <https://github.com/aGIToz/Pytorch_pdegraph>]

  • Amitoz Azad: Primal-Dual Algorithm for Total Variation Processing on Graphs [Jupyter <https://nbviewer.jupyter.org/github/aGIToz/Graph_Signal_Processing/tree/main>__]

  • Manan Goel: Recommending Amazon Products using Graph Neural Networks in :pyg:null PyTorch Geometric [:wandb:null W&B Report <https://wandb.ai/manan-goel/gnn-recommender/reports/Recommending-Amazon-Products-using-Graph-Neural-Networks-in-PyTorch-Geometric--VmlldzozMTA3MzYw>__]

  • Kùzu: Remote Backend for :pyg:null PyTorch Geometric [:colab:null Colab <https://colab.research.google.com/drive/12fOSqPm1HQTz_m9caRW7E_92vaeD9xq6>__]

  • Aniket Saxena: Graph Neural Networks-based Explanation App using :pyg:null PyTorch Geometric [Website <https://graph-explainability.streamlit.app/>, :github:null GitHub <https://github.com/fork123aniket/End-to-End-Node-and-Graph-Classification-and-Explanation-App>]

  • Mashaan Alshammari: Graph Attention in :pyg:null PyTorch Geometric [:youtube:null Youtube <https://youtu.be/AWkPjrZshug>, :github:null GitHub <https://github.com/mashaan14/YouTube-channel/blob/main/notebooks/2024_02_05_GAT.ipynb>]

  • Mashaan Alshammari: Graph Convolutional Networks (GCNs) in :pytorch:null PyTorch [:youtube:null Youtube <https://youtu.be/G6c6zk0RhRM>, :github:null GitHub <https://github.com/mashaan14/YouTube-channel/blob/main/notebooks/2023_12_04_GCN_introduction.ipynb>]

  • Mashaan Alshammari: GCN and SGC in :pytorch:null PyTorch [:youtube:null Youtube <https://youtu.be/PQT2QblNegY>, :github:null GitHub <https://github.com/mashaan14/YouTube-channel/blob/main/notebooks/2023_12_13_GCN_and_SGC.ipynb>],

  • Mashaan Alshammari: GCN Variants SGC and ASGC in :pytorch:null PyTorch [:youtube:null Youtube <https://youtu.be/ZNMV5i84fmM>, :github:null GitHub <https://github.com/mashaan14/YouTube-channel/blob/main/notebooks/2024_01_31_SGC_and_ASGC.ipynb>]