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Colab Notebooks and Video Tutorials — pytorch_geometric documentation

We have prepared a list of Colab notebooks that practically introduces you to the world of Graph Neural Networks with 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

7. Customizing Aggregations within Message Passing

8. Node Classification Instrumented with Weights&Biases

9. Graph Classification Instrumented with Weights&Biases

10. Link Prediction on MovieLens

11. Link Regression on MovieLens

All Colab notebooks are released under the MIT license.

The Stanford CS224W course has collected a set of graph machine learning tutorial blog posts, fully realized with PyG. Students worked on projects spanning all kinds of tasks, model architectures and applications. All tutorials also link to a Colab with the code in the tutorial for you to follow along with as you read it!

The PyTorch Geometric Tutorial project provides video tutorials and Colab notebooks for a variety of different methods in PyG:

1. Introduction [ YouTube, Colab]

2. PyTorch basics [ YouTube, Colab]

3. Graph Attention Networks (GATs) [ YouTube, Colab]

4. Spectral Graph Convolutional Layers [ YouTube, Colab]

5. Aggregation Functions in GNNs [ YouTube, Colab]

6. (Variational) Graph Autoencoders (GAE and VGAE) [ YouTube, Colab]

7. Adversarially Regularized Graph Autoencoders (ARGA and ARGVA) [ YouTube, Colab]

8. Graph Generation [ YouTube]

9. Recurrent Graph Neural Networks [ YouTube, Colab (Part 1), Colab (Part 2)]

10. DeepWalk and Node2Vec [ YouTube (Theory), YouTube (Practice), Colab]

11. Edge analysis [ YouTube, Colab (Link Prediction), Colab (Label Prediction)]

12. Data handling in PyG (Part 1) [ YouTube, Colab]

13. Data handling in PyG (Part 2) [ YouTube, Colab]

14. MetaPath2vec [ YouTube, Colab]

15. Graph pooling (DiffPool) [ YouTube, Colab]

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