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Graph Learning


Status Active. Full course - only reserve registrations
School IDA-gemensam (IDA)
Division AIICS
Owner Daniel Gnad
Homepage https://rlplab.com/teaching/phd_vt24/

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Course plan

No of lectures

Depending on the number of participants, there will be around 10 meetings.

Recommended for

PhD students who want to get a better understanding of different neural network architectures for graph inputs.


The aim of the course is to get a good understanding of the available neural network architectures that are suited of graph inputs. We want to understand the expressive power and limitations of different architectures and see how such networks can be applied in the context of symbolic reasoning methods.


A solid background in computer science, machine learning, and complexity theory is beneficial.


The course is run as a weekly seminar in which one participant presents a paper and leads a following discussion on the topic.


In this seminar, we cover different neural-network architectures for learning using graph representations. This includes, but is not limited to, the following topics:
Graph Convolutional Networks (GCN)
Graph Neural Networks (GNN)
Message-Passing Neural Networks (MPNN)
Graph Transformer Architectures
Expressive Power of different Architectures
Applications in Symbolic Reasoning


A selection of recent papers will be provided on the course webpage.


Presentation of a research article, possibly including some coding examples, leading the discussion on the presented topic, active participation in seminar discussions.


Daniel Gnad



Page responsible: Director of Graduate Studies