Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. - Johannes Klicpera; Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2018)

Predict then Propagate: Graph Neural Networks meet Personalized PageRank. - Johannes Klicpera; Aleksandar Bojchevski; Stephan Günnemann (ICLR (Poster), 2019)

Diffusion Improves Graph Learning. - Johannes Klicpera; Stefan Weißenberger; Stephan Günnemann (NeurIPS, 2019)

Directional Message Passing for Molecular Graphs. - Johannes Klicpera; Janek Groß; Stephan Günnemann (ICLR, 2020)

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. - Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICML, 2020)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. - Johannes Klicpera; Shankari Giri; Johannes T. Margraf; Stephan Günnemann (CoRR, 2020)

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. - Jan Schuchardt; Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICLR, 2021)

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. - Johannes Klicpera; Marten Lienen; Stephan Günnemann (ICML, 2021)

GemNet: Universal Directional Graph Neural Networks for Molecules. - Johannes Gasteiger; Florian Becker; Stephan Günnemann (NeurIPS, 2021)

Directional Message Passing on Molecular Graphs via Synthetic Coordinates. - Johannes Gasteiger; Chandan Yeshwanth; Stephan Günnemann (NeurIPS, 2021)

Influence-Based Mini-Batching for Graph Neural Networks. - Johannes Gasteiger; Chendi Qian; Stephan Günnemann (LoG, 2022)

How Do Graph Networks Generalize to Large and Diverse Molecular Systems? - Johannes Gasteiger; Muhammed Shuaibi; Anuroop Sriram; Stephan Günnemann; Zachary W. Ulissi; C. Lawrence Zitnick; Abhishek Das (CoRR, 2022)

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. - Johannes Gasteiger; Muhammed Shuaibi; Anuroop Sriram; Stephan Günnemann; Zachary W. Ulissi; C. Lawrence Zitnick; Abhishek Das (Trans. Mach. Learn. Res., 2022)

How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? - Sina Stocker; Johannes Gasteiger; Florian Becker; Stephan Günnemann; Johannes T. Margraf (Mach. Learn. Sci. Technol., 2022)

Ewald-based Long-Range Message Passing for Molecular Graphs. - Arthur Kosmala; Johannes Gasteiger; Nicholas Gao; Stephan Günnemann (ICML, 2023)

Accelerating Molecular Graph Neural Networks via Knowledge Distillation. - Filip Ekström Kelvinius; Dimitar Georgiev; Artur P. Toshev; Johannes Gasteiger (NeurIPS, 2023)

SubMix: Learning to Mix Graph Sampling Heuristics. - Sami Abu-El-Haija; Joshua V. Dillon; Bahare Fatemi; Kyriakos Axiotis; Neslihan Bulut; Johannes Gasteiger; Bryan Perozzi; MohammadHossein Bateni (UAI, 2023)

Challenges with unsupervised LLM knowledge discovery. - Sebastian Farquhar; Vikrant Varma; Zachary Kenton; Johannes Gasteiger; Vladimir Mikulik; Rohin Shah (CoRR, 2023)

On the Convergence of Structure and Geometry in Graph Neural Networks. - Johannes Gasteiger (2023)

Vereinigung von Struktur und Geometrie in Graph-Neuronalen Netzen. - Johannes Gasteiger (Ausgezeichnete Informatikdissertationen, 2023)

Attacking Large Language Models with Projected Gradient Descent. - Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Johannes Gasteiger; Stephan Günnemann (CoRR, 2024)

REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. - Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Vincent Cohen-Addad; Johannes Gasteiger; Stephan Günnemann (CoRR, 2025)

The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. - Tom Wollschläger; Jannes Elstner; Simon Geisler; Vincent Cohen-Addad; Stephan Günnemann; Johannes Gasteiger (CoRR, 2025)