4 citations · 10 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Crossfire: An Elastic Defense Framework for Graph Neural Networks Under Bit Flip Attacks
Lorenz Kummer, Samir Moustafa, Wilfried Gansterer +1
Bit Flip Attacks (BFAs) are a well-established class of adversarial attacks, originally developed for Convolutional Neural Networks within the computer vision domain. Most recently…