4 citations · 10 across the 9 of their papers we have counts for
9 papers
A Unifying Relational Perspective on Expressive Lottery Tickets
Lorenz Kummer, Samir Moustafa, Anatol Ehrlich +4
Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Stro…
Semi-Supervised Text-Attributed Graph Distillation
Yurui Lai, Samir Moustafa, Renchi Yang +1
{\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing representation learning methods ov…
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…
Accelerating Graph Neural Networks with a Novel Matrix Compression Format
João N. F. Alves, Samir Moustafa, Siegfried Benkner +3
The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse g…
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Lorenz Kummer, Samir Moustafa, Nils N. Kriege +1
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection att…
On the Two Sides of Redundancy in Graph Neural Networks
Franka Bause, Samir Moustafa, Johannes Langguth +2
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes i…