HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks

TL;DR

This paper introduces HRGCN, an unsupervised hierarchical graph neural network designed to detect anomalies in heterogeneous system behavior graphs, outperforming existing methods in real-world applications.

Contribution

The paper proposes a novel hierarchical relation-augmented GNN for heterogeneous graph anomaly detection, capturing complex entity interactions and relation types.

Findings

HRGCN outperforms state-of-the-art anomaly detection methods.

Effective in detecting anomalous network devices in industrial case studies.

Demonstrates robustness on real-world datasets.

Abstract

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.