Efficient Analysis of Latent Spaces in Heterogeneous Networks
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 identif...
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Zusammenfassung: | 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 exponential family distributions. |
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DOI: | 10.48550/arxiv.2412.02151 |