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

报告人：马舒洁

会议时间：2025年7月10日(周四)  10: 40

地点：综合楼644会议室

报告人简介：

马舒洁，加州大学河滨分校的统计学教授。她于2011年在密歇根州立大学获得统计学博士学位。她有广泛的研究领域，包括机器学习和深度学习的理论与应用、网络数据分析、面板数据分析、因果推断、精准医学、非参数和半参数回归以及高维数据分析。

报告摘要：

In this talk, I will introduce a new spectral clustering-based method called TransNet for transfer learning in community detection in network data. Our goal is to improve the clustering performance of the target network using auxiliary source networks, which are heterogeneous, privacy-preserved, and locally stored across various sources. The edges of each locally stored network are perturbed using the randomized response mechanism to achieve differential privacy.  Notably, we allow the source networks to have distinct privacy-preserving and heterogeneity levels as often desired in practice. To better utilize the information from the source networks, we propose a novel adaptive weighting method to aggregate the eigenspaces of the source networks multiplied by adaptive weights chosen to incorporate the effects of privacy and heterogeneity. We propose a regularization method that combines the weighted average eigenspace of the source networks with the eigenspace of the target network to achieve an optimal balance between them. Theoretically, we show that the adaptive weighting method enjoys the error-bound-oracle property in the sense that the error bound of the estimated eigenspace only depends on informative source networks.