Note
- nbviewer might have prettier math and image rendering than github
- Photos are taken from the resources (most of them from Stanford's cs224w and Graph representation learning – William Hamilton 2020) or the extra resources linked in the notebooks.
- Programming: OOP, being able to read and change functions to do something else.
- Math: Probabilities, statistics and linear algebra
- Graph theory – data structures
Part 1. Labs 1-5 -- Traditional generative methods, community detection.
Part 2 -- ML on graphs -- Embeddings, Classification, GNNs
Either make an anaconda env or a venv and install the requirements (Please do this before the lab)
| Lab | Link |
|---|---|
| 1 Intro | Linalg recap  networkx tutorial short probs recap python tutorial tutorial |
| 2 Graph measurements | Graph measurements |
| 3 Random_network_models | Random graphs |
| 4 Small worlds | Small worlds |
| 5 Communities | Communities |
| 6 DL intro | PyG intro Torch intro |
| 7 Embeddings | Node embeddings |
| 8 Node classification | Node classification |
| 9 Graph neural networks | GNN intro |
| 10 Graph neural networks | GNN2 Training GNN2 Training deepsnap Graph prediction Link prediction |
| 11 Knowledge graphs | Knowledge graphs TransE |
Open Graph Benchmark, ogb paper
Stanford course cs224w -- Big recommendation
Network science – Albert-Laszlo Barabasi
The atlas of the aspiring network scientist
Graph representation learning – William Hamilton 2020