Graph Reasoning Networks

• URL: http://arxiv.org/abs/2407.05816v1
• Date: Mon, 8 Jul 2024 10:53:49 GMT
• Title: Graph Reasoning Networks
• Authors: Markus Zopf, Francesco Alesiani,
• Abstract summary: Graph Reasoning Networks (GRNs) is a novel approach to combine the strengths of fixed and learned graph representations and a reasoning module based on a differentiable satisfiability solver. Results on real-world datasets show comparable performance to GNNs. Experiments on synthetic datasets demonstrate the potential of the newly proposed method.
• Score: 9.18586425686959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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