Colab Notebooks and Video Tutorials =================================== Official Examples ----------------- We have prepared a list of :colab:`Colab` notebooks that practically introduces you to the world of **Graph Neural Networks** with :pyg:`PyG`: 1. `Introduction: Hands-on Graph Neural Networks `__ 2. `Node Classification with Graph Neural Networks `__ 3. `Graph Classification with Graph Neural Networks `__ 4. `Scaling Graph Neural Networks `__ 5. `Point Cloud Classification with Graph Neural Networks `__ 6. `Explaining GNN Model Predictions using `__ :captum:`null` `Captum `__ 7. `Customizing Aggregations within Message Passing `__ 8. `Node Classification Instrumented with `__ :wandb:`null` `Weights&Biases `__ 9. `Graph Classification Instrumented with `__ :wandb:`null` `Weights&Biases `__ 10. `Link Prediction on MovieLens `__ 11. `Link Regression on MovieLens `__ All :colab:`Colab` notebooks are released under the MIT license. Stanford CS224W Tutorials ------------------------- .. image:: https://data.pyg.org/img/cs224w_tutorials.png :align: center :width: 941px :target: https://medium.com/stanford-cs224w .. raw:: html
The :stanford:`null` `Stanford CS224W `__ course has collected a set of `graph machine learning tutorial blog posts `__, fully realized with :pyg:`PyG`. Students worked on projects spanning all kinds of tasks, model architectures and applications. All tutorials also link to a :colab:`Colab` with the code in the tutorial for you to follow along with as you read it! PyTorch Geometric Tutorial Project ---------------------------------- The :pyg:`null` `PyTorch Geometric Tutorial `__ project provides **video tutorials and** :colab:`null` **Colab notebooks** for a variety of different methods in :pyg:`PyG`: 1. Introduction [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 2. :pytorch:`PyTorch` basics [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 3. Graph Attention Networks (GATs) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 4. Spectral Graph Convolutional Layers [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 5. Aggregation Functions in GNNs [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 6. (Variational) Graph Autoencoders (GAE and VGAE) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 7. Adversarially Regularized Graph Autoencoders (ARGA and ARGVA) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 8. Graph Generation [:youtube:`null` `YouTube `__] 9. Recurrent Graph Neural Networks [:youtube:`null` `YouTube `__, :colab:`null` `Colab (Part 1) `__, :colab:`null` `Colab (Part 2) `__] 10. DeepWalk and Node2Vec [:youtube:`null` `YouTube (Theory) `__, :youtube:`null` `YouTube (Practice) `__, :colab:`null` `Colab `__] 11. Edge analysis [:youtube:`null` `YouTube `__, :colab:`null` `Colab (Link Prediction) `__, :colab:`null` `Colab (Label Prediction) `__] 12. Data handling in :pyg:`PyG` (Part 1) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 13. Data handling in :pyg:`PyG` (Part 2) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 14. MetaPath2vec [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__] 15. Graph pooling (DiffPool) [:youtube:`null` `YouTube `__, :colab:`null` `Colab `__]