Efficient Analysis of Latent Spaces in Heterogeneous Networks
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909949795237888 |
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| author | Tian, Yuang Sun, Jiajin He, Yinqiu |
| author_facet | Tian, Yuang Sun, Jiajin He, Yinqiu |
| contents | This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under general distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Analysis of Latent Spaces in Heterogeneous Networks Tian, Yuang Sun, Jiajin He, Yinqiu Methodology This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and heterogeneous latent vectors are established simultaneously. The analysis framework offers remarkable flexibility, accommodating various types of edge weights under general distributions. |
| title | Efficient Analysis of Latent Spaces in Heterogeneous Networks |
| topic | Methodology |
| url | https://arxiv.org/abs/2412.02151 |