June 23, 2023
Max Planck Institute for Mathematics in the Sciences

Overview

Graph Neural Networks (GNNs) are powerful models for processing graph structured data. These models connect with topics such as geometric deep learning, geometry of data, spectral graph theory, network science, topology, etc. GNNs find a diversity of applications, in areas where data can be associated with graphs, such as molecules and drug discovery. In this mini meeting we plan a few talks on theoretical aspects, practical aspects, and application areas of GNNs, with space for discussions.

Goals

Theory of GNNs, current topics and open problems. E.g., expressive power, rewiring, pre-coloring, higher order graphs, simplicial complexes.

Current and future applications of GNNs. E.g., chemistry, chemical reaction networks, drug discovery. What are currently the main challenges where GNNs can contribute.

Further topics: GNN variants, transformers, optimization, infinite limits

Registration

This will be an in-person workshop. If you are interested in participating, please register using this online form. Registration open till June 16, 2023.

June 23, 2023
Max Planck Institute for Mathematics in the Sciences
G3 10 (Lecture hall)
Inselstr. 22
04103 Leipzig

Guido Montúfar
MPI for Mathematics in the Sciences

Pradeep Kr. Banerjee
MPI for Mathematics in the Sciences

Kedar Karhadkar
MPI for Mathematics in the Sciences

Administrative Contact