Community Membership Hiding via Gradient-based Optimization

Matteo Silvestri, Edoardo Gabrielli, Fabrizio Silvestri, Gabriele Tolomei

Abstract: We tackle the problem of \emph{community membership hiding}, which involves strategically altering a network's structure to obscure a target node's membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose \method{}, a gradient-based method that modifies the network's structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency.