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⚡️ Graph Machine Learning
Free advanced course: Machine learning on graphs .
The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.
▪ Introduction
▪ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
▪ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe
▪ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf