mlsanityData to 14 Oct 2026

Transfer learning under latent space model

arXiv 2509.15797 ↗First posted 19 Sep 2025stat.ME stat.MLcited by 0

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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.

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.

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References · 16

  1. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 90
  2. 2022Non-IID Transfer Learning on Graphscited by 43
  3. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38
  4. 2014Layer aggregation and reducibility of multilayer interconnected networkscited by 34
  5. 2020Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decompositioncited by 30
  6. 2016Network cross-validation by edge samplingcited by 26
  7. 2020Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embeddingcited by 19
  8. 2020Latent space models for multiplex networks with shared structurecited by 14
  9. 2024TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regressioncited by 11
  10. 2013Joint Modelling of Multiple Network Viewscited by 8
  11. 2021ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Networkcited by 8
  12. 2024Non-Euclidean Mixture Model for Social Network Embeddingcited by 4
4 more
  1. 2021Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detectioncited by 3
  2. 2024Transfer Learning for Latent Variable Network Modelscited by 3
  3. 2022Transfer learning for tensor Gaussian graphical modelscited by 2
  4. 2024Doubly unfolded adjacency spectral embedding of dynamic multiplex graphscited by 1

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