Graph Reasoning Networks
<|reference_start|>Graph Reasoning Networks: 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.<|reference_end|>
@article{zopf2024graph,
    title={Graph Reasoning Networks},
    author={Markus Zopf, Francesco Alesiani},
    journal={arXiv preprint arXiv:2407.05816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05816},
    primaryClass={cs.LG cs.AI}
}