Bayesian Deep Generative Models for Multiplex Networks with Multiscale Overlapping Clusters
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914486991978496 |
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| author | Zhou, Yuren Gu, Yuqi Dunson, David B. |
| author_facet | Zhou, Yuren Gu, Yuqi Dunson, David B. |
| contents | Our interest is in multiplex network data with multiple network samples observed across the same set of nodes. Examples originate from a variety of fields, including brain connectivity, international trade networks, and social networks, among others. Our goal is to infer a hierarchical structure of the nodes at a population level, while performing multi-resolution clustering of the individual replicates. To accomplish this, we propose a Bayesian hierarchical model, provide theoretical support in terms of identifiability and posterior consistency, and design efficient methods for posterior computation. We provide novel technical tools for proving model identifiability, which are of independent interest. Our proposed methodology is demonstrated through numerical simulation and an application to brain connectome data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Deep Generative Models for Multiplex Networks with Multiscale Overlapping Clusters Zhou, Yuren Gu, Yuqi Dunson, David B. Methodology Our interest is in multiplex network data with multiple network samples observed across the same set of nodes. Examples originate from a variety of fields, including brain connectivity, international trade networks, and social networks, among others. Our goal is to infer a hierarchical structure of the nodes at a population level, while performing multi-resolution clustering of the individual replicates. To accomplish this, we propose a Bayesian hierarchical model, provide theoretical support in terms of identifiability and posterior consistency, and design efficient methods for posterior computation. We provide novel technical tools for proving model identifiability, which are of independent interest. Our proposed methodology is demonstrated through numerical simulation and an application to brain connectome data. |
| title | Bayesian Deep Generative Models for Multiplex Networks with Multiscale Overlapping Clusters |
| topic | Methodology |
| url | https://arxiv.org/abs/2405.20936 |