3 papers
cs.DC2026
SIGMA: A Versatile Streaming Graph Partitioner for Vertex- and Edge-Balanced Distributed GNN Training
Barbara Hoffmann, Shai Dorian Peretz, Adil Chhabra +3
Distributed Graph Neural Network (GNN) training depends critically on how the underlying graph is partitioned across compute resources. Existing graph partitioners focus either on…
cs.LG2025
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…
cs.LG2024
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…