RABot: 通过强化学习引导的图增强进行不平衡和噪声的社会机器人检测
RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection
AI总结：
RABot通过强化学习引导的图增强方法，解决社会机器人检测中的类别不平衡和拓扑噪声问题，提升检测性能。
摘要：
社会机器人检测对于保护在线信息生态系统的真实性至关重要。尽管最近的图神经网络（GNN）解决方案取得了良好的成果，但仍然受到两个实际挑战的阻碍：（i）由于生成机器人成本高而导致的严重类别不平衡，以及（ii）由机器人精心模仿人类行为并伪造欺骗性链接引入的拓扑噪声。我们提出了Reinforcement-guided graph Augmentation social Bot detector（RABot），一种多粒度图增强框架，通过统一的方法解决这两个问题。RABot采用一种邻域感知的过采样策略，通过在局部子图内线性插值少数类嵌入，从而在低资源情况下稳定决策边界。同时，一个由强化学习驱动的边过滤模块结合基于相似性的边特征与自适应阈值优化，以在信息传递过程中剔除虚假交互，从而获得更干净的拓扑结构。在三个真实世界基准和四个GNN基础架构上的广泛实验表明，RABot在各方面都优于最先进的基线。此外，由于其增强和过滤模块与底层架构无关，RABot可以无缝集成到现有的GNN流程中，以最小的开销提高性能。
英文摘要：
Social bot detection is pivotal for safeguarding the integrity of online information ecosystems. Although recent graph neural network (GNN) solutions achieve strong results, they remain hindered by two practical challenges: (i) severe class imbalance arising from the high cost of generating bots, and (ii) topological noise introduced by bots that skillfully mimic human behavior and forge deceptive links. We propose the Reinforcement-guided graph Augmentation social Bot detector (RABot), a multi-granularity graph-augmentation framework that addresses both issues in a unified manner. RABot employs a neighborhood-aware oversampling strategy that linearly interpolates minority-class embeddings within local subgraphs, thereby stabilizing the decision boundary under low-resource regimes. Concurrently, a reinforcement-learning-driven edge-filtering module combines similarity-based edge features with adaptive threshold optimization to excise spurious interactions during message passing, yielding a cleaner topology. Extensive experiments on three real-world benchmarks and four GNN backbones demonstrate that RABot consistently surpasses state-of-the-art baselines. In addition, since its augmentation and filtering modules are orthogonal to the underlying architecture, RABot can be seamlessly integrated into existing GNN pipelines to boost performance with minimal overhead.
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