2 citations · 2 across the 5 of their papers we have counts for
5 papers
XIMP: Cross Graph Inter-Message Passing for Molecular Property Prediction
Anatol Ehrlich, Lorenz Kummer, Vojtech Voracek +2
Accurate molecular property prediction is central to drug discovery, yet graph neural networks often underperform in data-scarce regimes and fail to surpass traditional fingerprint…
Visualization and Analysis of the Loss Landscape in Graph Neural Networks
Samir Moustafa, Lorenz Kummer, Simon Fetzel +2
Graph Neural Networks (GNNs) are powerful models for graph-structured data, with broad applications. However, the interplay between GNN parameter optimization, expressivity, and ge…
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
Lorenz Kummer, Samir Moustafa, Anatol Ehrlich +4
The lottery ticket hypothesis (LTH) is well-studied for convolutional neural networks but has been validated only empirically for graph neural networks (GNNs), for which theoretica…
Efficient Mixed Precision Quantization in Graph Neural Networks
Samir Moustafa, Nils M. Kriege, Wilfried N. Gansterer
Graph Neural Networks (GNNs) have become essential for handling large-scale graph applications. However, the computational demands of GNNs necessitate the development of efficient…
On the Relationship Between Robustness and Expressivity of Graph Neural Networks
Lorenz Kummer, Wilfried N. Gansterer, Nils M. Kriege
We investigate the vulnerability of Graph Neural Networks (GNNs) to bit-flip attacks (BFAs) by introducing an analytical framework to study the influence of architectural features,…