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journal article Feb 16, 2023

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Jing Ren ORCID Feng Xia ORCID Ivan Lee ORCID Azadeh Noori Hoshyar ORCID Charu Aggarwal ORCID
ACM Transactions on Intelligent Systems and Technology Vol. 14 No. 2 pp. 1-29 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3570906
Abstract
Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.
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Showing 50 of 141 references

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Metrics
42
Citations
141
References
Details
Published
Feb 16, 2023
Vol/Issue
14(2)
Pages
1-29
License
View
Authors
J
Jing Ren

Federation University Australia, Ballarat, VIC, Australia

F
Feng Xia

Federation University Australia, Ballarat, VIC, Australia

I
Ivan Lee

University of South Australia, Adelaide, SA, Australia

A
Azadeh Noori Hoshyar

Federation University Australia, Ballarat, VIC, Australia

C
Charu Aggarwal

IBM T. J. Watson Research Center, New York, USA

Cite This Article
Jing Ren, Feng Xia, Ivan Lee, et al. (2023). Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges. ACM Transactions on Intelligent Systems and Technology, 14(2), 1-29. https://doi.org/10.1145/3570906
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