From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
About this item
Full title
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
Subjects
More information
Scope and Contents
Contents
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous patterns in complex graph data. To ad...
Alternative Titles
Full title
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2628405260
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2628405260
Other Identifiers
E-ISSN
2331-8422
