Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majority of samples. Meanwhile, anomalies can be easily detected when their behaviors differ from normality. Therefore, the performance can be further improved by enhancing the ability to learn the normal pattern. To this end, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation). Specifically, we first initialize the model with the contrastive networks on different scales. To provide sufficient and reliable normal nodes for normality learning, we design an effective hybrid strategy for normality selection. Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished. Eventually, extensive experiments on six benchmark graph datasets demonstrate the effectiveness of our normality learning-based scheme on GAD. Notably, the proposed algorithm improves the detection performance (up to 5.89% AUC gain) compared with the state-of-the-art methods. The source code is released at https://github.com/FelixDJC/NLGAD.

翻译：图异常检测（GAD）在机器学习与数据挖掘领域日益受到关注。现有研究主要关注如何捕获更丰富的信息以提升GAD中节点嵌入的质量。尽管检测性能取得显著进展，但针对任务特性的研究仍然相对匮乏。GAD旨在识别偏离大部分节点的异常样本，然而模型易习得占样本多数的正常模式。同时，当异常行为偏离正常模式时，检测将更为容易。因此，通过增强模型对正常模式的学习能力可进一步提升性能。为此，本文提出基于多尺度对比学习网络的正态性学习框架（简称NLGAD）。具体而言，我们首先利用不同尺度的对比网络初始化模型。为提供充足且可靠的正态节点用于正态性学习，我们设计了有效的混合策略进行正态性选择。最后，模型仅以可靠正态节点为输入进行精炼，学习更精确的正态性估计，从而更易区分异常节点。在六个基准图数据集上的大量实验证明了我们基于正态性学习方案在GAD中的有效性。值得注意的是，与最先进方法相比，所提算法将检测性能提升了（最高达5.89%的AUC增益）。源代码已发布于https://github.com/FelixDJC/NLGAD。