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

[2504.00890] Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search 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 robustness when some sources are highly heterogeneous or heavily privatized. We further show that the error bound of \texttt{TransNet} is no greater than that of estimators using only the target network or only (weighted) sources. Empirically, \texttt{TransNet} delivers strong gains across a range of privacy levels and heterogeneity patterns. For completeness, we also present \texttt{TransNetX}, an extension based on Gaussian perturbation of projection matrices under the assumption that trusted local data curators are available. Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG) Cite as: arXiv:2504.00890 [stat.ML] (or arXiv:2504.00890v2 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2504.00890 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xiao Guo [ view email ] [v1] Tue, 1 Apr 2025 15:19:07 UTC (5,667 KB) [v2] Tue, 14 Apr 2026 05:58:34 UTC (2,895 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2025-04 Change to browse by: cs cs.LG stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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