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

Li, J; Pang, G; Chen, L; Namazi-Rad, M-R

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

Li, J Pang, G Chen, L

Namazi-Rad, M-R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023, 00, pp. 1-10
Issue Date:: 2023-11-06

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Field	Value	Language
dc.contributor.author	Li, J
dc.contributor.author	Pang, G
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Namazi-Rad, M-R
dc.date	2023-10-09
dc.date.accessioned	2024-03-04T22:35:00Z
dc.date.available	2024-03-04T22:35:00Z
dc.date.issued	2023-11-06
dc.identifier.citation	2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023, 00, pp. 1-10
dc.identifier.isbn	9798350345032
dc.identifier.uri	http://hdl.handle.net/10453/176090
dc.description.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
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP210101347
dc.relation.ispartof	2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
dc.relation.ispartof	2023 IEEE 10th International Conference on Data Science and Advanced Analytics
dc.relation.isbasedon	10.1109/dsaa60987.2023.10302626
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Thessaloniki, Greece
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-11-06T00:00:00+1000Z
dc.date.updated	2024-03-04T22:34:59Z
pubs.finish-date	2023-10-13
pubs.publication-status	Published
pubs.start-date	2023-10-09
pubs.volume	00

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

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http://hdl.handle.net/10453/176090