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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.10546 |
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| _version_ | 1866913673618915328 |
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| author | Alain, Mathieu Takao, So Dong, Xiaowen Rieck, Bastian Noutahi, Emmanuel |
| author_facet | Alain, Mathieu Takao, So Dong, Xiaowen Rieck, Bastian Noutahi, Emmanuel |
| contents | The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed by transforming spatial features from the graph domain into spectral features in the Euclidean domain, and using them as the input points of classical kernels. However, this approach currently only takes into account features on vertices, whereas some graph datasets also support features on edges. In this work, we present a Gaussian process-based classification algorithm that can leverage one or both vertex and edges features. Furthermore, we take advantage of the Hodge decomposition to better capture the intricate richness of vertex and edge features, which can be beneficial on diverse tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Classification Gaussian Processes via Hodgelet Spectral Features Alain, Mathieu Takao, So Dong, Xiaowen Rieck, Bastian Noutahi, Emmanuel Machine Learning The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed by transforming spatial features from the graph domain into spectral features in the Euclidean domain, and using them as the input points of classical kernels. However, this approach currently only takes into account features on vertices, whereas some graph datasets also support features on edges. In this work, we present a Gaussian process-based classification algorithm that can leverage one or both vertex and edges features. Furthermore, we take advantage of the Hodge decomposition to better capture the intricate richness of vertex and edge features, which can be beneficial on diverse tasks. |
| title | Graph Classification Gaussian Processes via Hodgelet Spectral Features |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.10546 |