A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Ma, X; Wu, J; Xue, S; Yang, J; Zhou, C; Sheng, QZ; Xiong, H; Akoglu, L

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Ma, X Wu, J Xue, S Yang, J Zhou, C Sheng, QZ Xiong, H Akoglu, L

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (12), pp. 12012-12038
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Ma, X
dc.contributor.author	Wu, J
dc.contributor.author	Xue, S
dc.contributor.author	Yang, J
dc.contributor.author	Zhou, C
dc.contributor.author	Sheng, QZ
dc.contributor.author	Xiong, H
dc.contributor.author	Akoglu, L
dc.date.accessioned	2024-03-21T23:53:55Z
dc.date.available	2024-03-21T23:53:55Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (12), pp. 12012-12038
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/176994
dc.description.abstract	Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines. Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data in the features space and inherently overlooks the structural information. Graphs have been prevalently used to preserve structural information, and this raises the graph anomaly detection problem - identifying anomalous graph objects (nodes, edges, sub-graphs, and graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data. For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We also highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2021.3118815
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-03-21T23:53:51Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	12

Abstract:

Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines. Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data in the features space and inherently overlooks the structural information. Graphs have been prevalently used to preserve structural information, and this raises the graph anomaly detection problem - identifying anomalous graph objects (nodes, edges, sub-graphs, and graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data. For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. We also highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

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http://hdl.handle.net/10453/176994