Graph Neural Network Study Tutorial

 

Stanford CS224W Tutorials

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!

PyTorch Geometric Tutorial Project

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]