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 . Students worked on projects spanning all kinds of tasks, model architectures and applications. All tutorials also link to a  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 :

  1. Introduction [ YouTube Colab]

  2.  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  (Part 1) [ YouTube Colab]

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

  14. MetaPath2vec [ YouTube Colab]

  15. Graph pooling (DiffPool) [ YouTube Colab]

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