Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the diversity of anomalies and the dearth of labeled data. Existing methodologies - reconstruction-based and contrastive learning - while effective, often suffer from efficiency issues, stemming from their complex objectives and elaborate modules. To improve the efficiency of GAD, we introduce a simple method termed PREprocessing and Matching (PREM for short). Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities. Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage. Moreover, through rigorous evaluations of five real-world datasets, our method demonstrated robustness and effectiveness. Notably, when validated on the ACM dataset, PREM achieved a 5% improvement in AUC, a 9-fold increase in training speed, and sharply reduce memory usage compared to the most efficient baseline.

翻译：节点级图异常检测（GAD）在医学、社交网络和电子商务等领域中，从图结构数据中识别异常节点发挥着关键作用。然而，由于异常类型的多样性以及标注数据的匮乏，相关挑战日益凸显。现有的基于重构和对比学习的方法虽有效，但常因其复杂的优化目标和精巧的模块设计而面临效率问题。为提升GAD的效率，我们提出了一种名为预处理与匹配（简称PREM）的简单方法。我们的方法精简了GAD流程，在保持强大异常检测能力的同时，降低了时间和内存消耗。PREM由预处理模块和自邻域匹配两个模块组成，消除了训练过程中信息传递传播的需求，并采用简单的对比损失函数，从而显著减少了训练时间和内存使用。此外，通过在五个真实数据集上的严格评估，我们的方法展现了鲁棒性和有效性。值得注意的是，在ACM数据集上的验证表明，与最高效的基线方法相比，PREM在AUC上提升了5%，训练速度提高了9倍，并大幅降低了内存使用。