Graphs in Artificial Intelligence
Neural Networks

Current events:

Currently, no events are planned, but you can check out the recordings of our past events below.

Past events:

Workshop: Explainability and Applicability of Graph Neural Networks, 06.09.23 to 08.09.23 in Kassel, Germany

We had a wonderful time full of great talks, discussions and a sunny tour with amazing views through the Bergpark Wilhelmshöhe. Thank you so much to our speakers Paul Almasan, Balthazar Donon, Simon Geisler, Dominik Köhler, Alan Perotti, Martin Ritzert, Emanuele Rossi, Petar Veličković, Soledad Villar and of course all the participants!

For those who missed the workshop we provide the recordings of most talks below. The corresponding slides are available in PDF format by clicking on the title of the talk.

Workshop: Hot Topics in Graph Neural Networks, 25.10.2022

In this Workshop, the work of the GAIN group is addressed containing dynamic GNN models, the expressivity of GNN models, and their application in the power grid. Among others, the speakers enlightened us with their work on Algorithmically-aligned GNNs, the Improvement of Message-passing, and Geometric Machine Learning for Molecules.
For this we invited great speakers in the field of graph neural networks, with Petar Veličković, Fabian Jogl, Maximilian Thiessen, Massimo Perini, Antonio Longa and Hannes Stärk.

In case you have missed this amazing Workshop we provide the recordings of all Talks below. The corresponding slides are available in PDF format by clicking on the title of the talk.

Kick-Off AI-Junior-Research-Group GAIN, 05.05.2021 to 07.05.2021

For those who missed the introduction of our group, the exciting talks on the topic of graphs, graph neural networks and their applications in neuroscience, physics and renewable energies, we have some recordings of the talks below.

For the available slides in PDF format just click on the title of the respective presentation. The invited speakers were top-level scientists including Franco Scarselli, Viola Priesemann, Matthias Gebhardt, Marián Boguñá, Michael Bronstein, Martin Braun, Alexander Scheidler and Daniel Martinez. With their talks they cover topics from Graph Neural Networks to Quantum Gravity and Power Supply Networks.

Network Geometry
Marián Boguña

Geometric Deep Learning
Michael Bronstein

Networks in Renewable Energies
Martin Braun, Alexander Scheidler

Spin Networks
Daniel Martinez


We are funded by the Federal Ministry of Education and Research Germany (BMBF) under the following funding codes:
01IS20047A, according to the 'Policy for the funding of female junior researchers in Artificial Intelligence',
16ME0877, according to the KMU-innovativ' guideline.
We are funded by the Federal Ministry for Economic Affairs and Climate Action(BMWK) under the following funding code:
020E-100626677, within the '7. Energieforschungsprogramm'.

The responsibility for the content of this website or of any publication lies with the author.