Evading Community Detection via Counterfactual Neighborhood Search
This addresses privacy concerns for users on social media platforms who wish to avoid community detection without leaving the platform, though it is incremental as it builds on existing methods for graph manipulation.
The paper tackles the problem of community membership hiding by strategically altering network graphs to prevent nodes from being identified by community detection algorithms, using a constrained counterfactual graph objective solved via deep reinforcement learning, with experiments showing it outperforms existing baselines in balancing accuracy and cost.
Community detection techniques are useful 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 preserve their anonymity and opt out of community detection for various reasons, such as affiliation with political or religious organizations, without leaving the platform. 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. Extensive experiments demonstrate that our method outperforms existing baselines, striking the best balance between accuracy and cost.