<ul><li>This paper presents TransNet, a new method for transfer learning in community detection of network data.</li><li>TransNet aims to improve the clustering performance of the target network by utilizing auxiliary source networks that are privacy-preserved and locally stored across various sources.</li><li>To achieve privacy preservation, the edges of each locally stored network are perturbed using the randomized response mechanism, ensuring differential privacy.</li><li>By proposing an adaptive weighting method and regularization technique, TransNet effectively aggregates the eigenspaces of the source networks, incorporating the effects of privacy and heterogeneity.</li></ul>

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

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