TIEJ5100 COM1: Graph Neural Networks (JSS34) (1 cr)
Study level:
Postgraduate studies
Grading scale:
Pass - fail
Language:
English
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2025-2026
Description
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
Learning outcomes
Understanding main concepts and methods in relational learning
Description of prerequisites
Minimum: basic level machine learning courses. Recommended: advanced courses on machine learning and deep learning
Study materials
- William L. Hamilton, Graph Representation Learning. Synthesis Lectures on AI and ML, Vol. 14, No. 3. 2020.
- Michael M. Bronstein and Joan Bruna and Taco Cohen and Petar Veličković, Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. ArXiv, 2021.
- L. Wu, P. Cui, J. Pei, and L. Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore, 2022
- M. Hajij et. al., Topological Deep Learning: Going Beyond Graph Data. ArXiv, 2023.
- Stefanie Jegelka. Theory of Graph Neural Networks: Representation and Learning. ArXiv, 2022.
Completion methods
Method 1
Description:
Lectures and demonstrations. Each student is required to give a presentation on the final day.
Evaluation criteria:
Pass/fail
Time of teaching:
Period 1
Select all marked parts
Parts of the completion methods
x
Participation in teaching (1 cr)
Type:
Participation in teaching
Grading scale:
Pass - fail
Evaluation criteria:
<p>Pass/fail</p>
Language:
English
Study methods:
Lectures and demonstrations. Each student is required to give a presentation on the final day.
Study materials:
- William L. Hamilton, Graph Representation Learning. Synthesis Lectures on AI and ML, Vol. 14, No. 3. 2020.
- Michael M. Bronstein and Joan Bruna and Taco Cohen and Petar Veličković, Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. ArXiv, 2021.
- L. Wu, P. Cui, J. Pei, and L. Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore, 2022
- M. Hajij et. al., Topological Deep Learning: Going Beyond Graph Data. ArXiv, 2023.
- Stefanie Jegelka. Theory of Graph Neural Networks: Representation and Learning. ArXiv, 2022.