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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.20895 |
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| _version_ | 1866912376360534016 |
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| author | Dilworth, Emerald Davis, Ed Lawson, Daniel J. |
| author_facet | Dilworth, Emerald Davis, Ed Lawson, Daniel J. |
| contents | Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made and the underlying data generating function is unknown. We address this problem by considering embeddings of the observed and bootstrapped network that are statistically indistinguishable. We utilise an exchangeable network test that can empirically validate bootstrap samples generated by any method. Existing methods fail this test, so we propose a principled, distribution-free network bootstrap using k-nearest neighbour smoothing, that can pass this exchangeable network test in many synthetic and real-data scenarios. We demonstrate the utility of this work in combination with the popular data visualisation method t-SNE, where uncertainty estimates from bootstrapping are used to explain whether visible structures represent real statistically sound structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20895 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Valid Bootstraps for Network Embeddings with Applications to Network Visualisation Dilworth, Emerald Davis, Ed Lawson, Daniel J. Computation Applications Machine Learning Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made and the underlying data generating function is unknown. We address this problem by considering embeddings of the observed and bootstrapped network that are statistically indistinguishable. We utilise an exchangeable network test that can empirically validate bootstrap samples generated by any method. Existing methods fail this test, so we propose a principled, distribution-free network bootstrap using k-nearest neighbour smoothing, that can pass this exchangeable network test in many synthetic and real-data scenarios. We demonstrate the utility of this work in combination with the popular data visualisation method t-SNE, where uncertainty estimates from bootstrapping are used to explain whether visible structures represent real statistically sound structures. |
| title | Valid Bootstraps for Network Embeddings with Applications to Network Visualisation |
| topic | Computation Applications Machine Learning |
| url | https://arxiv.org/abs/2410.20895 |