Structural Node Embeddings with Homomorphism Counts

no code implementations • 29 Aug 2023 • Hinrikus Wolf, Luca Oeljeklaus, Pascal Kühner, Martin Grohe

Grohe (PODS 2020) proposed the theoretical foundations for using homomorphism counts in machine learning on graph level as well as node level tasks.

Graph Learning Interpretable Machine Learning

Solving AC Power Flow with Graph Neural Networks under Realistic Constraints

no code implementations • 14 Apr 2022 • Luis Böttcher, Hinrikus Wolf, Bastian Jung, Philipp Lutat, Marc Trageser, Oliver Pohl, Andreas Ulbig, Martin Grohe

In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow.

Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing

1 code implementation • 17 Feb 2021 • Jan Tönshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe

As the theoretical basis for our approach, we prove a theorem stating that the expressiveness of CRaWl is incomparable with that of the Weisfeiler Leman algorithm and hence with graph neural networks.

Graph Classification Graph Learning +2

The Effects of Randomness on the Stability of Node Embeddings

2 code implementations • 20 May 2020 • Tobias Schumacher, Hinrikus Wolf, Martin Ritzert, Florian Lemmerich, Jan Bachmann, Florian Frantzen, Max Klabunde, Martin Grohe, Markus Strohmaier

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 algorithms and graphs.

General Classification Node Classification

Graph Neural Networks for Maximum Constraint Satisfaction

1 code implementation • 18 Sep 2019 • Jan Toenshoff, Martin Ritzert, Hinrikus Wolf, Martin Grohe

Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems.

Combinatorial Optimization