Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.

翻译：图异常检测（Graph Anomaly Detection, GAD）已在欺诈检测、网络安全、金融安全和生物化学等多个领域取得成功并广泛应用。然而，现有的图异常检测算法侧重于区分单个实体（节点或图），忽视了图中可能存在异常群体的情况。为解决这一局限，本文针对一项新任务——群体级图异常检测（Group-level Graph Anomaly Detection, Gr-GAD），提出了一种新颖的无监督框架。该框架首先采用图自编码器（Graph AutoEncoder, GAE）的一种变体，通过捕捉长程不一致性来定位属于潜在异常群体的锚节点。随后，采用群体采样方法对候选群体进行采样，并将其输入所提出的基于拓扑模式的图对比学习（Topology Pattern-based Graph Contrastive Learning, TPGCL）方法中。TPGCL利用群体的拓扑模式作为线索，为每个候选群体生成嵌入表示，从而区分不同的异常群体。在真实数据集和合成数据集上的实验结果表明，所提出的框架在识别和定位异常群体方面展现出优越性能，凸显了其作为Gr-GAD任务一种有前景解决方案的潜力。所提出框架的数据集和代码已开源至GitHub仓库：https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection。