  Graph neural networks (GNNs) are the predominant approach for graph-based
machine learning. While neural networks have shown great performance at
learning useful representations, they are often criticized for their limited
high-level reasoning abilities. In this work, we present Graph Reasoning
Networks (GRNs), a novel approach to combine the strengths of fixed and learned
graph representations and a reasoning module based on a differentiable
satisfiability solver. While results on real-world datasets show comparable
performance to GNN, experiments on synthetic datasets demonstrate the potential
of the newly proposed method.
