The mini-course covers the basics of Graph Representation Learning / Graph Neural Networks.

The following topics are included:

• Motivation for graph representation learning. Several examples of benchmarks.
• Brief review of graph and matrix theory, notion of embeddings.
• Necessary NLP models reminder: from word2vec to Transformers.
• Random Walk Graph Embeddings: node2vec, struct2vec.
• Message Passing Embeddings: GraphSAGE, Graph Attention Network.
• Unsupervised Graph Embeddings, notion of Triplet Loss.

Repository is organized as follows:

• Graph_Slides.pdf - materials for the lectures
• Seminars:
  1. PyTorch_Geometric_Dataset.ipynb - introduces CORA dataset, covers the creation of PyTorch Geometric Dataset based on this graph. This dataset will be used in the following seminars for model training.
  2. GraphModel_Supervised.ipynb - acquaints with NeighborSampler and the concept of mini-batch learning on large graphs. Then the training process of supervised Graph Attention Network is covered.
  3. GraphModel_Unsupervised.ipynb - covers training of unsupervised GraphSAGE model. Visualization of resulting node embeddings using UMAP is included.

We recommend to download ipynb files for correct/proper display of HTML elements.