How to Get Started With Graph ML - Blog Walkthrough

This course provides a high-level, research-oriented introduction to graph machine learning. It discusses applications, graph embeddings, graph neural networks, geometric deep learning, research resources, datasets, and a graph attention network project.

Syllabus

Research/learning challenges
What is Graph ML? We're all graphs
Cool Graph ML applications
Fake news and fundamental science
Halicin a potent antibiotic discovered by a GNN
Contrasting Graph ML with CV and NLP
Resources - graph embedding methods
Graph Neural Networks
Top to bottom approach - high level resources
Spatial methods
Simple baselines sometimes work great!
Parallel with CNNs
GNN expressivity
Dynamic graphs
Unsupervised graph learning and geometric DL
Datasets/benchmarks and newsletter
GAT project
Related research subfields