Graph Reasoning Networks

Markus Zopf; Francesco Alesiani

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arXiv · 2407.05816

Graph Reasoning Networks

Markus Zopf·Francesco Alesiani

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Abstract

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.

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Markus Zopf, Francesco Alesiani. 2024-07-08. Graph Reasoning Networks. https://arxiv.org/abs/2407.05816

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