RAGC: Robust Attributed Graph Clustering
What it is
RAGC is a framework for robust attributed graph clustering that incorporates Hybrid-Collaborative Augmentation (HCA) and Contrastive Sample Adaptive-Differential Awareness (CSADA). It addresses the challenge of clustering graphs with noisy attributes and complex structures.
The problem it solves
Standard graph clustering methods often struggle with noisy attributes and complex graph structures.
What you could use it for
- Robust graph clustering in the presence of noisy attributes
- Graph representation learning for attributed networks
- Community detection in complex social or biological networks
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