HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical
  Relation-augmented Graph Neural Networks
Jiaxi Li Guansong Pang Ling Chen Mohammad-Reza Namazi-Rad
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.