Abstract: Semi-supervised graph anomaly detection aims to identify anomalous nodes in graph structures by leveraging limited labeled data and abundant unlabeled data. Due to the scarcity of labeled samples and highly imbalanced class distributions, existing methods still face significant performance bottlenecks in practical applications. Although self-training is one of the classic methods for effectively utilizing unlabeled data, it still faces two key challenges in the context of graph anomaly detection. On the one hand, the limited labeled samples contain only a small number of anomalous nodes, and its long-tail distribution can easily bias the model toward the normal class during the initial training stage, resulting in insufficient discrimination ability to identify anomalous nodes. On the other hand, during training with class imbalance, the continuous accumulation of erroneous pseudo labels triggers a “Matthew effect,” causing the model to continuously amplify its own prediction bias. To address these challenges, this paper proposes DISC, a robust semi-supervised graph anomaly detection self-training framework that tackles the dual imbalance at the optimization objective level by decoupling supervised signals from different sources. Specifically, a focal loss is introduced for the limited labeled samples to enhance the model’s attention to scarce anomalous nodes. For high-confidence pseudo-labeled samples, a Distribution-Aware Rectification (DAR) loss is designed to calibrate class priors based on the frequency of pseudo-label classes. Combined with reverse cross-entropy, this approach improves the model’s robustness against noisy pseudo labels, thereby suppressing the progressive amplification of prediction bias during self-training. Experiments are conducted on two public benchmark datasets, YelpChi and Reddit, with comparisons against eight representative methods. The results show that DISC maintains strong competitiveness across different graph structures and anomaly ratios. Specifically, it achieves an AUROC of 81.89% on YelpChi, and an AUPRC of 5.29% and a Macro-F1 of 51.96% on Reddit, with the latter two metrics achieving the best performance. Ablation study and visualization results further validate the effectiveness of the proposed components, demonstrating that DISC can effectively alleviate both label scarcity and bias accumulation, while improving detection performance and robustness under limited supervision settings.

摘要： 半监督图异常检测旨在利用有限标记数据与大量未标记数据，在复杂图结构中识别异常节点。受制于标记样本稀缺与类别分布高度不平衡，现有方法在实际应用中仍面临显著性能瓶颈。自训练虽然是有效利用未标记数据的经典方法之一，但在图异常检测场景下仍面临两项关键挑战：一方面，有限标记样本中异常节点数量较少，长尾分布容易使模型在初始训练阶段偏向正常类，导致对异常节点的识别能力不足；另一方面，在类别不平衡的训练过程中，错误伪标签的持续累积会引发“马太效应”，使模型不断放大自身预测偏差。针对上述挑战，本文提出一种鲁棒的半监督图异常检测自训练框架DISC，通过解耦不同来源的监督信号，从优化目标层面应对双重不平衡问题。具体而言，针对有限标记样本引入焦点损失以增强模型对稀疏异常节点的关注；针对高置信度伪标签样本，设计分布感知校正损失（Distribution-Aware Rectification，DAR），根据伪标签类别频率对类别先验进行校正，并结合反向交叉熵提高模型对噪声伪标签的鲁棒性，从而抑制自训练过程中预测偏差的持续放大。在YelpChi和Reddit两个公开图异常检测基准数据集上开展实验，并与八种代表性方法进行比较。实验结果表明，DISC在不同数据结构和异常比例下均表现出较强竞争力，其中在YelpChi上取得81.89%的AUROC，在Reddit上取得5.29%的AUPRC和51.96%的Macro-F1，后两项指标均达到最优。消融实验与可视化结果进一步验证了各模块的有效性，表明DISC能够有效缓解半监督图异常检测中的少标签不平衡与自训练偏差累积问题，并提升标注稀缺场景下的检测性能和鲁棒性。