Community Concealment from Unsupervised Graph Learning-Based Clustering

TL;DR

This paper proposes a method to conceal communities in attributed graphs to protect group privacy from GNN-based clustering, by rewiring edges and modifying features to reduce community detectability.

Contribution

It introduces a perturbation strategy that effectively conceals communities in graphs, outperforming existing methods like DICE under similar constraints.

Findings

The method achieves 20-45% median concealment improvement.

Community concealment is influenced by boundary connectivity and feature similarity.

The approach demonstrates effectiveness on synthetic and real-world networks.

Abstract

Graph neural networks (GNNs) are designed to use attributed graphs to learn representations. Such representations are beneficial in the unsupervised learning of clusters and community detection. Nonetheless, such inference may reveal sensitive groups, clustered systems, or collective behaviors, raising concerns regarding group-level privacy. Community attribution in social and critical infrastructure networks, for example, can expose coordinated asset groups, operational hierarchies, and system dependencies that could be used for profiling or intelligence gathering. We study a defensive setting in which a data publisher (defender) seeks to conceal a community of interest while making limited, utility-aware changes in the network. Our analysis indicates that community concealment is strongly influenced by two quantifiable factors: connectivity at the community boundary and feature similarity between the protected community and adjacent communities. Informed by these findings, we present a perturbation strategy that rewires a set of selected edges and modifies node features to reduce the distinctiveness leveraged by GNN message passing. The proposed method outperforms DICE in our experiments on synthetic benchmarks and real network graphs under identical perturbation budgets. Overall, it achieves median relative concealment improvements of approximately 20-45% across the evaluated settings. These findings demonstrate a mitigation strategy against GNN-based community learning and highlight group-level privacy risks intrinsic to graph learning.