Community Membership Hiding as Counterfactual Graph Search via Deep
  Reinforcement Learning
Andrea Bernini Fabrizio Silvestri Gabriele Tolomei
Community detection techniques are useful tools for social media platforms to
discover tightly connected groups of users who share common interests. However,
this functionality often comes at the expense of potentially exposing
individuals to privacy breaches by inadvertently revealing their tastes or
preferences. Therefore, some users may wish to safeguard their anonymity and
opt out of community detection for various reasons, such as affiliation with
political or religious organizations.
  In this study, we address the challenge of community membership hiding, which
involves strategically altering the structural properties of a network graph to
prevent one or more nodes from being identified by a given community detection
algorithm. We tackle this problem by formulating it as a constrained
counterfactual graph objective, and we solve it via deep reinforcement
learning. We validate the effectiveness of our method through two distinct
tasks: node and community deception. Extensive experiments show that our
approach overall outperforms existing baselines in both tasks.