COM1: Graph Neural Networks

About

Relational learning

Overview
Graph neural networks (GNNs)
Spectral perspective
Spatial perspective
Main architectures
Dealing with oversmoothing and oversquashing
Theory of GNNs
Expressivity
Generalization
Extensions
High-order GNNs
GNNs for knowledge graphs
Graph Transformers
Hypergraph neural networks
Topological neural networks
Generative models for graphs

Target group

Minimum: basic level machine learning courses. Recommended: advanced courses on machine learning and deep learning.

Advanced Bachelor’s students, Master’s students, PhD students and post-docs

Course aim

Understanding main concepts and methods in relational learning