1. Introduction to python, numpy and pandas (Basic operations) Lab sheet,

  2. Introduction to graphs using networkx, eignevectors/eigenvalues computation Lab sheet,

  3. Dimensionality reduction - Comparison of PCA vs Laplacian Eigenmaps. Lab sheet,

  4. Classification using Label Propagation Algorithm (LPA) Paper link and Label Spreading Algorithm (LSA) PaperLink. Lab sheet Lab sheet,
  5. Text classification using error and label propagation (C&S model) research paper. Lab sheet Lab sheet,
  6. Error and label propagation (C&S - with scaling) Lab sheet,
  7. Leveraging Cross-Entropy and Supervised Contrastive Loss for classification. Lab sheet,
  8. Iterative spectral clustering vs K-Means based spectral clustering. Lab sheet,
  9. Laplacian-Eigenmap-based decoder for link prediction in unirelational graph. Lab sheet,
  10. Rescal decoder model for graph reconstruction in multi-relational graph. Lab sheet,
  11. Graph convolutional Networks- Pytorch implementation on Cora dataset. Lab sheet,
  12. PageRank-with and without teleportation; Lab sheet,
  13. Non-linear data generation and construction of a similarity graph. Lab sheet,
  14. Spectral clustering on half moon data, Lab sheet,
  15. Spectral clustering vs Girvan-Newman clustering, Lab sheet,
  16. Similarity graph using K-nearest neighbor, Lab sheet,
  17. Random walk based shallow embedding model implementation Reference (section 3.3), Lab sheet,