This is my learning record for the Machine Learning with Graphs in Stanford.

The official site: https://web.stanford.edu/class/cs224w/

🎥: https://youtu.be/JAB_plj2rbA

🏷 && ⌨️: https://web.stanford.edu/class/cs224w/

• Colab 0
• Colab 1
• Colab 2
• Colab 3
• Colab 4
• Colab 5

• Message passing in GraphML. I think it is the most important conception in GNN. The ppts are very intuitive and concise which are quite worth reading
• PyG. In the Colabs, we will use this package quite often, not only at high-level but also at low-level. To finish the Colabs, you usually need to check the docs, which make you familiar with PyG
• GCN/GAT/GraphSAGE We will implement these models in the Colabs
• PageRank, Knowledge graph, etc. This course covers many topics in GNN.

🤗 Welcome to check my repo cs-courses.

Join me and enjoy the journey 🚀