4 papers · 1 filter
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
Nikolai Merkel, Ruben Mayer, Volker Markl +1
Graph Neural Networks (GNNs) are widely used for learning on graph-structured data, but scaling GNN training to massive graphs remains challenging. To enable scalable distributed t…
Comparing Methods for Bias Mitigation in Graph Neural Networks
Barbara Hoffmann, Ruben Mayer
This paper examines the critical role of Graph Neural Networks (GNNs) in data preparation for generative artificial intelligence (GenAI) systems, with a particular focus on address…
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Jana Vatter, Mykhaylo Zayats, Marcos MartÃnez Galindo +4
With the ever-growing size of real-world graphs, numerous techniques to overcome resource limitations when training Graph Neural Networks (GNNs) have been developed. One such appro…
Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act
Barbara Hoffmann, Jana Vatter, Ruben Mayer
The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning…