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

ArXi:2504.00890v2 Announce Type: replace-cross 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 d, and privacy-preserved auxiliary \emph{source networks