Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow methods that could not effectively capture the complex interdependency of graph data or graph autoencod...
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Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
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TN_cdi_proquest_journals_2563971096
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2563971096
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2108.09896
