This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about the system operations as possible. Detecting anomalous heterogeneous graphs from a large set of system behaviour graphs is crucial for many real-world applications like online web/mobile service and cloud access control. To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs. HRGCN trains a hierarchical relation-augmented Heterogeneous Graph Neural Network (HetGNN), which learns better graph representations by modelling the interactions among all the system entities and considering both source-to-destination entity (node) types and their relation (edge) types. Extensive evaluation on two real-world application datasets shows that HRGCN outperforms state-of-the-art competing anomaly detection approaches. We further present a real-world industrial case study to justify the effectiveness of HRGCN in detecting anomalous (e.g., congested) network devices in a mobile communication service. HRGCN is available at https://github.com/jiaxililearn/HRGCN.

翻译：本文研究了异构图级异常检测问题。异构图通常用于表示复杂工业系统中不同类型实体间的行为，以尽可能捕获系统运行信息。从大量系统行为图中检测异常异构图对于许多实际应用（如在线网页/移动服务和云访问控制）至关重要。为解决该问题，我们提出了HRGCN——一种无监督深度异构图神经网络，用于建模系统中不同实体间的复杂异构关系，从而有效识别这些异常行为图。HRGCN训练了一个层级关系增强的异构图神经网络（HetGNN），通过建模所有系统实体间的交互，并同时考虑源到目标实体（节点）类型及其关系（边）类型，来学习更好的图表示。在两个真实应用数据集上的广泛评估表明，HRGCN优于最先进的竞争性异常检测方法。我们进一步展示了一个实际工业案例研究，以证明HRGCN在检测移动通信服务中异常（如拥塞）网络设备方面的有效性。HRGCN代码开源在https://github.com/jiaxililearn/HRGCN。