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.

翻译：图神经网络（GNN）是基于图的机器学习的主流方法。尽管神经网络在学习有效表示方面表现出卓越性能，但其有限的高层推理能力常受诟病。本研究提出图推理网络（GRNs），这是一种创新方法，旨在融合固定图表示与学习图表示的优势，并集成基于可微分可满足性求解器的推理模块。虽然在真实数据集上的结果显示出与GNN相当的性能，但在合成数据集上的实验证明了新提出方法的潜力。