Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913841767514112 |
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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 | Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs suffer from overfitting, leading to poor performance. Recently, Gaussian processes (GPs) with graph-level inputs have been proposed as an alternative. In this work, we extend the Gaussian process framework to simplicial complexes (SCs), enabling the handling of edge-level attributes and attributes supported on higher-order simplices. We further augment the resulting SC representations by considering their Hodge decompositions, allowing us to account for homological information, such as the number of holes, in the SC. We demonstrate that our framework enhances the predictions across various applications, paving the way for GPs to be more widely used for graph and SC-level predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations Alain, Mathieu Takao, So Dong, Xiaowen Rieck, Bastian Noutahi, Emmanuel Machine Learning Artificial Intelligence Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs suffer from overfitting, leading to poor performance. Recently, Gaussian processes (GPs) with graph-level inputs have been proposed as an alternative. In this work, we extend the Gaussian process framework to simplicial complexes (SCs), enabling the handling of edge-level attributes and attributes supported on higher-order simplices. We further augment the resulting SC representations by considering their Hodge decompositions, allowing us to account for homological information, such as the number of holes, in the SC. We demonstrate that our framework enhances the predictions across various applications, paving the way for GPs to be more widely used for graph and SC-level predictions. |
| title | Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10877 |