Dual autoencoder with reconstruction-level contrast.

Left: an attributed network G and an augmented view produced by graph data augmentation. Middle: a shared graph-attention encoder yields node embeddings Z, which feed two decoders: a structure decoder reconstructing and an attribute decoder reconstructi...

Полное описание

Сохранить в:

Библиографические подробности
Главный автор:	Hossein Rafieizadeh (22676722) (author)
Другие авторы:	Hadi Zare (20073000) (author), Mohsen Ghassemi Parsa (22676725) (author), Hocine Cherifi (8177628) (author)
Опубликовано:	2025
Предметы:	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
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

Dual autoencoder with reconstruction-level contrast.

Схожие документы