异构网络中潜在空间的高效分析
Efficient Analysis of Latent Spaces in Heterogeneous Networks
AI总结：
本文提出了一种统一框架，用于高效估计异构网络中共享和异构潜在向量，通过分解潜在向量并利用分数方程提高统计效率。
摘要：
本文提出了一种统一的框架，用于在异构网络的潜在空间建模中实现高效的估计。我们考虑了一类将潜在向量分解为共享和网络特定成分的潜在空间模型。我们开发了一种新的程序，首先识别共享的潜在向量，然后通过高效的分数方程进一步细化估计，以实现统计效率。同时建立了估计共享和异构潜在向量的Oracle误差率。分析框架具有显著的灵活性，能够适应各种类型的边权重在一般分布下的情况。
英文摘要：
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.
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