[2504.00890] Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks

Statistics > Machine Learning arXiv:2504.00890 (stat) [Submitted on 1 Apr 2025 (v1), last revised 14 Apr 2026 (this version, v2)] Title:Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks Authors:Xiao Guo, Xuming He, Xiangyu Chang, Shujie Ma View a PDF of the paper titled Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks, by Xiao Guo and 3 other authors View PDF HTML (experimental) Abstract:Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves community detection on a \emph{target network} by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary \emph{source networks}. Our focus is the \textit{local differential privacy} regime, in which each local data provider perturbs edges via \textit{randomized response} before release, requiring no trusted third party. \texttt{TransNet} aggregates source eigenspaces through a novel adaptive weighting scheme that accounts for both privacy and heterogeneity, and then regularizes the weighted source eigenspace with the target eigenspace to optimally balance the two. Theoretically, we establish an error-bound-oracle property: the estimation error for the aggregated eigenspace depends only on \textit{informative sources}, ensuring ro...