7 citations · 7 across the 3 of their papers we have counts for
4 papers
ALINC: Active Learning for Inductive Node Classification via Graph Sampling
Pascal Plettenberg, Denis Huseljic, André Alcalde +2
Active learning (AL) for node classification typically focuses on selecting the most informative nodes for annotation within one or a few large graphs (e.g., in social network anal…
Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics
Pascal Plettenberg, André Alcalde, Bernhard Sick +1
The design and optimization of Printed Circuit Board (PCB) schematics is crucial for the development of high-quality electronic devices. Thereby, an important task is to optimize d…
Flow-Attentional Graph Neural Networks
Pascal Plettenberg, Dominik Köhler, Bernhard Sick +1
Graph Neural Networks (GNNs) have become essential for learning from graph-structured data. However, existing GNNs do not consider the conservation law inherent in graphs associate…
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…