Efficient Analysis of Latent Spaces in Heterogeneous Networks
Abstract
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.
Fields of Study & Subjects
Natural Language Processing Techniques
Data Mining Algorithms and Applications
Topic Modeling
Publication details
- Journal / source
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Pages
- 1–13
- Publication date
- 12 January 2026
- Language
- EN