少样本消息增强对比学习用于图异常检测
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
AI总结：
提出FMGAD模型，结合自监督对比学习与Deep-GNN消息增强重建，利用少量标注异常有效检测图异常，在六个数据集上优于现有方法。
摘要：
图异常检测在识别图数据中显著偏离多数实例的异常实例方面发挥着关键作用。它在信息安全领域已获得广泛关注，包括网络入侵、金融欺诈和恶意评论等。由于获取标注数据的挑战，现有方法主要以无监督方式开发。由于无监督方式缺乏先验知识指导，识别出的异常可能被证明是数据噪声或单个数据实例。在现实场景中，可以捕获有限批次的标注异常，因此研究图异常检测中的少样本问题至关重要。利用这一潜力，我们提出了一种新颖的少样本图异常检测模型，称为FMGAD（少样本消息增强对比图异常检测器）。FMGAD在视图内和跨视图利用自监督对比学习策略来捕获内在且可迁移的结构表示。此外，我们提出了Deep-GNN消息增强重建模块，该模块充分利用少样本标签信息，并实现长距离传播以将监督信号传播到更深层的未标注节点。该模块反过来有助于自监督对比学习的训练。在六个真实世界数据集上的综合实验结果表明，无论异常是人工注入的还是领域固有的，FMGAD都能取得优于其他最先进方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
