# Transfer learning under latent space model

arXiv: https://arxiv.org/abs/2509.15797 · HTML: https://mlsanity.com/paper/2509.15797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Non-IID Transfer Learning on Graphs](https://mlsanity.com/paper/2212.08174/) (2022)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition](https://mlsanity.com/paper/2002.04457/) (2020)
- [Network cross-validation by edge sampling](https://mlsanity.com/paper/1612.04717/) (2016)
- [Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding](https://mlsanity.com/paper/2007.02914/) (2020)
- [Latent space models for multiplex networks with shared structure](https://mlsanity.com/paper/2012.14409/) (2020)
- [TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression](https://mlsanity.com/paper/2404.01153/) (2024)
- [Joint Modelling of Multiple Network Views](https://mlsanity.com/paper/1301.3759/) (2013)
- [ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network](https://mlsanity.com/paper/2102.10226/) (2021)
- [Non-Euclidean Mixture Model for Social Network Embedding](https://mlsanity.com/paper/2411.04876/) (2024)
- [Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection](https://mlsanity.com/paper/2109.15136/) (2021)
- [Transfer Learning for Latent Variable Network Models](https://mlsanity.com/paper/2406.03437/) (2024)
- [Transfer learning for tensor Gaussian graphical models](https://mlsanity.com/paper/2211.09391/) (2022)
- [Doubly unfolded adjacency spectral embedding of dynamic multiplex graphs](https://mlsanity.com/paper/2410.09810/) (2024)

## Abstract (arXiv)

Latent space model plays a crucial role in network analysis, and accurate estimation of latent variables is essential for downstream tasks such as link prediction. However, the large number of parameters to be estimated presents a challenge, especially when the latent space dimension is not exceptionally small. In this paper, we propose a transfer learning method that leverages information from networks with latent variables similar to those in the target network, thereby improving the estimation accuracy for the target. Given transferable source networks, we introduce a two-stage transfer learning algorithm that accommodates differences in node numbers between source and target networks. In each stage, we derive sufficient identification conditions and design tailored projected gradient descent algorithms for estimation. Theoretical properties of the resulting estimators are established. When the transferable networks are unknown, a detection algorithm is introduced to identify suitable source networks. Simulation studies and analyses of two real datasets demonstrate the effectiveness of the proposed methods.