Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Additionally, there are far fewer anomalous nodes than normal nodes, indicating a long-tailed data distribution. To address these challenges, a unique algorithm,Decoupled Self-supervised Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection. DSLAD employs bilinear pooling and masked autoencoder as the anomaly discriminators. By decoupling anomaly discrimination and representation learning, a balanced feature space is constructed, in which nodes are more semantically discriminative, as well as imbalance issue can be resolved. Experiments conducted on various six benchmark datasets reveal the effectiveness of DSLAD.

翻译：属性图上的异常检测因其实际应用价值而成为一个关键课题。现有方法主要侧重于异常判别而忽略表示学习，导致语义混合和类别不平衡问题。这与同配性假设相冲突，即异常节点通常直接与正常节点相连。此外，异常节点数量远少于正常节点，呈现长尾数据分布。为解决这些挑战，本文提出一种独特算法——解耦自监督异常检测（DSLAD）。DSLAD是一种自监督方法，将异常判别与表示学习解耦用于异常检测。该方法采用双线性池化和掩码自编码器作为异常判别器。通过解耦异常判别与表示学习，构建了一个平衡的特征空间，在该空间中节点具有更强的语义区分性，同时不平衡问题得以解决。在六个标准基准数据集上的实验证明了DSLAD的有效性。