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

Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose textttTransNet, a new spectral clustering-based transfer learning framework that improves community detection on a target network by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary source networks. Our focus is the textitlocal differential privacy regime, in which each local data provider perturbs edges via textitrandomized response before release, requiring no trusted third party. textttTransNet 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 textitinformative sources, ensuring robustness when some sources are highly heterogeneous or heavily privatized. We further show that the error bound of textttTransNet is no greater than that of estimators using only the target network or only (weighted) sources. Empirically, textttTransNet delivers strong gains across a range of privacy levels and heterogeneity patterns. For completeness, we also present textttTransNetX, an extension based on Gaussian perturbation of projection matrices under the assumption that trusted local data curators are available.