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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.05816 |
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| _version_ | 1866929413733482496 |
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| author | Zopf, Markus Alesiani, Francesco |
| author_facet | Zopf, Markus Alesiani, Francesco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Reasoning Networks Zopf, Markus Alesiani, Francesco Machine Learning Artificial Intelligence 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. |
| title | Graph Reasoning Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.05816 |