Cited By
View all- In YYoon KKim KShin KPark CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Self-Guided Robust Graph Structure RefinementProceedings of the ACM Web Conference 202410.1145/3589334.3645522(697-708)Online publication date: 13-May-2024
Class Label-aware Graph Anomaly Detection
Pages 4008 - 4012
Abstract
Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such anomaly labels, but also the absence of class labels (the class a node belongs to used in a general node classification task). In this work, we study the utility of class labels for unsupervised GAD; in particular, how they enhance the detection of structural anomalies. To this end, we propose a Class Label-aware Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of labeled nodes to enhance the performance of unsupervised GAD. Extensive experiments on ten datasets demonstrate the superior performance of CLAD in comparison to existing unsupervised GAD methods, even in the absence of ground-truth class label information. The source code for CLAD is available at https://github.com/jhkim611/CLAD.
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Index Terms
- Class Label-aware Graph Anomaly Detection
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Information & Contributors
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Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Publication History
Published: 21 October 2023
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- Institute of Information & communications Technology Planning & Evaluation (IITP)
- National Research Foundation of Korea (NRF)
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
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View all- In YYoon KKim KShin KPark CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Self-Guided Robust Graph Structure RefinementProceedings of the ACM Web Conference 202410.1145/3589334.3645522(697-708)Online publication date: 13-May-2024
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