用代理节点注入技术隐藏社交图中重叠社区成员身份，保护隐私。
Evading Overlapping Community Detection via Proxy Node Injection
- 采用深度强化学习学习添加代理节点的策略，保持图结构不变。
- 在真实数据集上，对重叠社区的隐藏效果优于现有方法。
- 适用于需保护用户社区归属隐私的社交网络场景。
保护社交图中的隐私需要防止敏感信息（如社区归属）被图分析推断出来，同时不显著改变图的拓扑结构。我们通过解决‘社区成员隐藏’（CMH）问题来应对这一挑战，旨在通过边修改使目标节点脱离其原始社区，无论使用何种检测算法。以往研究主要关注非重叠社区检测，此时简单策略通常足够，但现实图更符合重叠社区模型，此类策略失效。据我们所知，这是首个在重叠社区设定下形式化并解决CMH的问题。本文提出一种深度强化学习（DRL）方法，学习有效的修改策略，包括使用代理节点，同时保留图结构。在真实数据集上的实验表明，该方法在有效性和效率上均显著优于现有基线，为具有重叠社区的隐私保护图修改提供了系统性工具。
原文摘要 · Abstract (English)
Protecting privacy in social graphs requires preventing sensitive information, such as community affiliations, from being inferred by graph analysis, without substantially altering the graph topology. We address this through the problem of \emph{community membership hiding} (CMH), which seeks edge modifications that cause a target node to exit its original community, regardless of the detection algorithm employed. Prior work has focused on non-overlapping community detection, where trivial strategies often suffice, but real-world graphs are better modeled by overlapping communities, where such strategies fail. To the best of our knowledge, we are the first to formalize and address CMH in this setting. In this work, we propose a deep reinforcement learning (DRL) approach that learns effective modification policies, including the use of proxy nodes, while preserving graph structure. Experiments on real-world datasets show that our method significantly outperforms existing baselines in both effectiveness and efficiency, offering a principled tool for privacy-preserving graph modification with overlapping communities.
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