Programming Exercises

1. Introduction to python, numpy and pandas (Basic operations) Code
2. Introduction to graphs using networkx, eignevectors/eigenvalues computation Lab sheet, Code
3. Principal Component Analysis (PCA) - for visualization and dimensionality reduction Lab material, Solution
4. Dimensionality reduction - Comparison of PCA vs Laplacian Eigenmaps. Lab material, Solution
5. Classification using Label Propagation Algorithm (LPA) Paper link and Label Spreading Algorithm (LSA) PaperLink. Lab sheet Link, Solution Link
6. Text classification using Correct & Smooth approach based on research paper. Lab sheet Link, Solution Link
7. Correct & Smooth (with spectral embeddings and scaling) Labsheet, Solution
8. Leveraging Cross-Entropy and Supervised Contrastive Loss for classification. Labsheet, Solution
9. Laplacian-Eigenmap based decoder for link prediction in unirelational graph. Labsheet, Unsolved jupyter file, Solution
10. Rescal decoder model for graph reconstruction in multi-relational graph. Labsheet, Unsolved jupyter file, Solution