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

Music City Center 

In this talk, I will introduce a new spectral clustering-based method for transfer learning in community detection of network data. Our goal is to improve the clustering performance of the target network using auxiliary source networks, which are locally stored across various sources, privacy-preserved, and heterogeneous. We allow the source networks to have distinct privacy and heterogeneity levels that often happen in practice. To better utilize the information from the heterogeneous and privacy-preserved source networks, we propose a novel adaptive weighting method to first aggregate the eigenspaces of the source networks multiplied by different weights chosen to incorporate the effects of privacy and heterogeneity. Then we propose a regularization method that combines the weighted average eigenspace of the source networks with the eigenspace of the target network to automatically achieve an optimal balance between them. Theoretically, we show that the adaptive weighting method enjoys the oracle property, where the bound of estimated eigenspace only depends on informative source networks, and the adaptive weighting strategy leads to an order-wise smaller bound compared to the equal weighting strategy. We also demonstrate that the bound of the estimated eigenspace is tighter than either the weighted average of eigenspace of source networks or the eigenspace of the target network alone.

Community detection

Heterogeneity

Privacy

Distributed learning

transfer learning