Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.

翻译：图异常检测在识别图数据中显著偏离大多数实例的异常节点方面具有关键作用，已在网络安全入侵、金融欺诈、恶意评论等众多信息安全领域获得广泛关注。由于标注数据获取困难，现有方法主要基于无监督方式开发。然而无监督方式缺乏先验知识指导，检测出的异常可能实为数据噪声或孤立数据实例。现实场景中可获取少量标注异常样本，这使得探究图异常检测中的少样本问题至关重要。基于这一潜在优势，我们提出一种新型少样本图异常检测模型FMGAD（基于少样本消息增强对比学习的图异常检测器）。该模型通过视图内与视图间的自监督对比学习策略，捕获内在且可迁移的结构表征。此外，我们提出深度图神经网络消息增强重建模块，该模块充分利用少样本标注信息，通过长距离传播将监督信号扩散至更深层的未标注节点，进而反哺自监督对比学习训练。在六个真实世界数据集上的全面实验结果表明，无论面对人工注入异常还是领域固有异常，FMGAD均能取得优于现有最优方法的性能表现。