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

2025-04-01

AI Summary

This work addresses privacy-preserving community detection via transfer learning across multi-source heterogeneous networks. Methodologically: (1) we propose an adaptive weighted fusion mechanism that dynamically balances source network heterogeneity and transfer utility under differential privacy (DP) budget constraints, using randomized response for perturbation; (2) we formulate a regularized joint feature estimation model and theoretically establish its error bound with optimal selectivity. Our key contribution is the first integration of DP-perturbed heterogeneous network transfer learning with spectral clustering—simultaneously ensuring rigorous privacy guarantees, accurate modeling of structural heterogeneity, and statistical interpretability. Experiments demonstrate that our approach significantly outperforms both single-network spectral clustering and naive weighted transfer baselines under stringent privacy constraints, achieving superior community detection accuracy and robustness.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper develops a new spectral clustering-based method called TransNet 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 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. We also demonstrate that TransNet performs better than the estimator using only the target network and the estimator using only the weighted source networks.

Problem

• Transfer learning for community detection in networks
• Privacy-preserving data handling with differential privacy
• Adaptive weighting to balance source and target networks

Innovation

• Spectral clustering-based transfer learning method
• Randomized response for differential privacy
• Adaptive weighting of heterogeneous source networks