69 citations · 69 across the 4 of their papers we have counts for
4 papers
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Jana Vatter, Mykhaylo Zayats, Marcos Martínez 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…
Choosing a Classical Planner with Graph Neural Networks
Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen +2
Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers vari…
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen
Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the…