Graph anomaly detection (GAD) has been widely applied in many areas, e.g., fraud detection in finance and robot accounts in social networks. Existing methods are dedicated to identifying the outlier nodes that deviate from normal ones. While they heavily rely on high-quality annotation, which is hard to obtain in real-world scenarios, this could lead to severely degraded performance based on noisy labels. Thus, we are motivated to cut the edges of suspicious nodes to alleviate the impact of noise. However, it remains difficult to precisely identify the nodes with noisy labels. Moreover, it is hard to quantitatively evaluate the regret of cutting the edges, which may have either positive or negative influences. To this end, we propose a novel framework REGAD, i.e., REinforced Graph Anomaly Detector. Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels. (i) We design a tailored action and search space to train a policy network to carefully prune edges step by step, where only a few suspicious edges are prioritized in each step. (ii) We design a policy-in-the-loop mechanism to iteratively optimize the policy based on the feedback from base detector. The overall performance is evaluated by the cumulative rewards. Extensive experiments are conducted on three datasets under different anomaly ratios. The results indicate the superior performance of our proposed REGAD.

翻译：图异常检测（GAD）已广泛应用于诸多领域，例如金融欺诈检测和社交网络中的机器人账户。现有方法致力于识别偏离正常节点的离群节点。然而，这些方法严重依赖高质量标注，而在现实场景中高质量标注难以获取，这可能导致基于含噪标签的性能严重下降。因此，我们旨在通过剪除可疑节点的边来缓解噪声的影响。然而，精确识别含噪标签的节点仍然困难。此外，难以定量评估剪除边的代价，因为剪边可能产生正面或负面影响。为此，我们提出了一种新颖的框架REGAD，即强化图异常检测器。具体而言，我们的目标是通过剪除通过高置信度标签节点近似得到的含噪边，来最大化基础检测器的性能提升（AUC）。(i) 我们设计了一个定制的动作与搜索空间，以训练策略网络逐步谨慎地剪除边，其中每一步仅优先处理少量可疑边。(ii) 我们设计了一种策略在环机制，基于基础检测器的反馈迭代优化策略。整体性能通过累积奖励进行评估。我们在三种不同异常比例的数据集上进行了大量实验。结果表明，我们提出的REGAD具有优越的性能。