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.

翻译：社区检测技术是社交媒体平台发现紧密关联且具有共同兴趣用户群体的有效工具，然而这项功能往往以潜在暴露用户品味或偏好为代价，导致隐私泄露风险。因此，部分用户可能出于政治或宗教组织关联等缘由，希望保护自身匿名性并规避社区检测。本研究针对社区成员身份隐藏这一挑战问题展开研究，通过策略性调整网络图结构属性，使特定节点或多个节点能够规避给定社区检测算法的识别。我们将该问题形式化为带约束的反事实图优化目标，并采用深度强化学习方法求解。通过节点欺骗和社区欺骗两项独立任务验证了所提方法的有效性，大量实验结果表明，本方法在两项任务中均显著优于现有基线方法。