This repository contains labs and practical assignments on Advanced Graph Neural Networks course.

Labs:

1. Self-supervised learning on graphs
2. Subgraph embeddings
3. Scalable GNN
4. Knowledge distillation for GNN
5. Deep generative graph models
6. Interpretable explanations for GNN
7. Recommender systems based on GNN
8. Temporal graph embeddings
9. Query embeddings for knowledge graphs
10. Combinatorial optimization using GNN

All assignments are presented as Jupyter notebooks, that can be done by writing code instead of the line

# YOUR CODE HERE

All notebooks contain test cells with assert statements that help you understand whether your code is correct.

Assignments:

1. Graph contrastive learning
2. Deep recurrent graph generation
3. Knowledge graphs for recommender systems
4. Multi-Hop logical reasoning in knowledge graphs