24 citations · 91 across the 28 of their papers we have counts for
6 papers · 1 filter
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
Dominik Fuchsgruber, Tim Poštuvan, Stephan Günnemann +1
Many applications in traffic, civil engineering, or electrical engineering revolve around edge-level signals. Such signals can be categorized as inherently directed, for example, t…
Revisiting the Robust Alignment of Circuit Breakers
Leo Schwinn, Simon Geisler
Over the past decade, adversarial training has emerged as one of the few reliable methods for enhancing model robustness against adversarial attacks [Szegedy et al., 2014, Madry et…
Adversarial Robustness of Graph Transformers
Philipp Foth, Lukas Gosch, Simon Geisler +2
Existing studies have shown that Message-Passing Graph Neural Networks (MPNNs) are highly susceptible to adversarial attacks. In contrast, despite the increasing importance of Grap…
Spatio-Spectral Graph Neural Networks
Simon Geisler, Arthur Kosmala, Daniel Herbst +1
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive fi…
Explainable Graph Neural Networks Under Fire
Zhong Li, Simon Geisler, Yuhang Wang +2
Predictions made by graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. In an attempt to tackl…
Attacking Large Language Models with Projected Gradient Descent
Simon Geisler, Tom Wollschläger, M. H. I. Abdalla +2
Current LLM alignment methods are readily broken through specifically crafted adversarial prompts. While crafting adversarial prompts using discrete optimization is highly effectiv…