Stanford cs224w : machine learning with graphs 筆記

Lecture 1 : https://github.com/tom99763/cs224w/blob/main/notes/L1-1.%20Graph_Representation.pdf

Lecture 2 : 

    Node-Level Feature : https://github.com/tom99763/cs224w/blob/main/notes/L2-1.%20Node_Level_Feature.pdf

    Link_Level Feature : https://github.com/tom99763/cs224w/blob/main/notes/L2-2.%20Link_Level_Feature.pdf

    Graph_Level Feature : https://github.com/tom99763/cs224w/blob/main/notes/L2-3.%20Graph_Level_Feature.pdf

Lecture 3 :  https://github.com/tom99763/cs224w/blob/main/notes/L3-1.%20Node%20Embedding.pdf

Lecture 4 : https://github.com/tom99763/cs224w/blob/main/notes/L4-1.%20PageRank_Graph_As_Matrix.pdf

Lecture 5 : https://github.com/tom99763/cs224w/blob/main/notes/L5-1.%20Message_Passing_Node_Classification.pdf

Lecture 6、7 : https://github.com/tom99763/cs224w/blob/main/notes/L_6_7-1.%20Deep%20learning%20for%20graphs.pdf

 

2021/6/10 更新colab1 code, 對應lecture 1~4 : https://github.com/tom99763/cs224w/blob/main/colab/lecture1_4_colab.ipynb

用Matrix Factorization 弄的 Node Embedding :

                                      

用Embedding去train一個SVM, predict test set的一些unlabeled的nodes (node classification task） :