HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
This addresses anomaly detection in complex industrial systems like online services and cloud access control, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of detecting anomalous heterogeneous graphs in industrial systems by proposing HRGCN, an unsupervised deep heterogeneous graph neural network that models complex relations between entities. It shows that HRGCN outperforms state-of-the-art anomaly detection approaches on two real-world datasets and demonstrates effectiveness in a case study for detecting congested network devices in mobile communication services.
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.