Graph Neural Networks Mini Meeting at MPI MiS


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.



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

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

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



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


Pradeep Kr. Banerjee


Nithya Bhasker

National Center for Tumor Diseases (NCT/UCC) Dresden

Tyler Derr

Vanderbilt University

Kedar Karhadkar


Yunchao Lance Liu

Vanderbilt University

Jens Meiler

Leipzig University / Vanderbilt University

Guillermo Restrepo


Stefanie Speidel

NCT Dresden

Yuguang Wang


Bingxin Zhou

Shanghai Jiao Tong University


09:00 - 09:15 Guido Montúfar (Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany)
09:15 - 09:45 Yuguang Wang (SJTU)
Applied harmonic analysis and particle dynamics for designing neural message passing on graphs
09:45 - 10:00
10:00 - 10:30 Pradeep Kr. Banerjee (MPI MiS)
On Oversquashing in Message Passing Graph Neural Networks
10:30 - 11:00 Kedar Karhadkar (Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany)
First-order spectral rewiring for addressing oversquashing in GNNs
11:00 - 13:00
13:00 - 13:30 Bingxin Zhou (Shanghai Jiao Tong University, Shanghai, China)
Mutational Effect Prediction and Directed Evolution with Geometric Deep Learning
13:30 - 14:00 Nithya Bhasker (National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany)
Application of Graph Neural Networks to surgical complication prediction
14:00 - 14:30 Guillermo Restrepo (MPI MiS, Leipzig, Germany)
Graph neural networks in chemistry, some of their applications and challenges
14:30 - 14:45
14:45 - 15:15 Yunchao Lance Liu (Vanderbilt University, Nashville, USA), Jens Meiler (Leipzig University/ Vanderbilt University)
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
15:15 - 15:45 Tyler Derr (Vanderbilt University)
Towards Data-Centric Graph Learning for Real-World Applications

Scientific Organizers

Guido Montúfar

Max Planck Institute for Mathematics in the Sciences

Pradeep Kr. Banerjee

Max Planck Institute for Mathematics in the Sciences

Kedar Karhadkar

Max Planck Institute for Mathematics in the Sciences

Administrative Contact

Katharina Matschke

Max Planck Institute for Mathematics in the Sciences Contact via Mail