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 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…
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 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…