20 citations · 46 across the 6 of their papers we have counts for
6 papers
End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
Hinrikus Wolf, Luis Böttcher, Sarra Bouchkati +9
In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, w…
Structural Node Embeddings with Homomorphism Counts
Hinrikus Wolf, Luca Oeljeklaus, Pascal Kühner +1
Graph homomorphism counts, first explored by Lovász in 1967, have recently garnered interest as a powerful tool in graph-based machine learning. Grohe (PODS 2020) proposed the theo…
Solving AC Power Flow with Graph Neural Networks under Realistic Constraints
Luis Böttcher, Hinrikus Wolf, Bastian Jung +5
In this paper, we propose a graph neural network architecture to solve the AC power flow problem under realistic constraints. To ensure a safe and resilient operation of distributi…
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
Jan Tönshoff, Martin Ritzert, Hinrikus Wolf +1
We propose CRaWl, a novel neural network architecture for graph learning. Like graph neural networks, CRaWl layers update node features on a graph and thus can freely be combined o…
The Effects of Randomness on the Stability of Node Embeddings
Tobias Schumacher, Hinrikus Wolf, Martin Ritzert +6
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorith…
Graph Neural Networks for Maximum Constraint Satisfaction
Jan Toenshoff, Martin Ritzert, Hinrikus Wolf +1
Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems. We introduce a graph neural network architecture for solving such optim…