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...

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Bibliografski detalji
Glavni autor:	Hossein Rafieizadeh (22676722) (author)
Daljnji autori:	Hadi Zare (20073000) (author), Mohsen Ghassemi Parsa (22676725) (author), Hocine Cherifi (8177628) (author)
Izdano:	2025
Teme:	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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Opis
Sažetak:	<p>Left: an attributed network <i>G</i> and an augmented view produced by graph data augmentation. Middle: a shared graph-attention encoder yields node embeddings <i>Z</i>, which feed two decoders: a structure decoder reconstructing and an attribute decoder reconstructing for the two views, yielding and . Right: reconstruction-level contrast compares, for each node <i>i</i>, the reconstructions via and ; it minimizes <i>D</i> when and , and enforces a learnable margin <i>m</i> when or . Color coding: green nodes denote non-augmented nodes; red nodes denote augmented nodes; the dotted green arc indicates minimization of <i>D</i>; the dashed orange arc indicates margin enforcement; cross-hatched bars mark augmented features; gray edges are neutral; blue heatmaps depict reconstructed matrices.</p>

Dual autoencoder with reconstruction-level contrast.

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