Proposes an attention-based contrastive framework fusing multi-scale local topological signals and global cluster prototypes, trained with dual-view losses, reporting competitive results on eight graph datasets.
[Mrabahet al., 2022 ] Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, and Riadh Ksantini
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Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
Proposes an attention-based contrastive framework fusing multi-scale local topological signals and global cluster prototypes, trained with dual-view losses, reporting competitive results on eight graph datasets.