Structural and node-level augmentations for graph anomalies.

<p>Left: original attributed network <i>G</i>. Right: augmented attributed network . Middle: augmentation methods: (1) feature copying (attribute mimicking across distant nodes); (2) feature scaling (multiplying or dividing continuous attributes); (3) node isolation (dropping all i...

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Bibliographic Details
Main Author:	Hossein Rafieizadeh (22676722) (author)
Other Authors:	Hadi Zare (20073000) (author), Mohsen Ghassemi Parsa (22676725) (author), Hocine Cherifi (8177628) (author)
Published:	2025
Subjects:	Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns
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Structural and node-level augmentations for graph anomalies.

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