MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929692923133952 |
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| author | Abate, Carlo Bianchi, Filippo Maria |
| author_facet | Abate, Carlo Bianchi, Filippo Maria |
| contents | We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph topology and can find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, trainable end-to-end, and particularly suitable for downstream tasks on heterophilic graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05100 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks Abate, Carlo Bianchi, Filippo Maria Machine Learning We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph topology and can find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, trainable end-to-end, and particularly suitable for downstream tasks on heterophilic graphs. |
| title | MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.05100 |