AMGC2: Rethinking Deep Graph Clustering With a High Attribute-Missing Ratio.
Attribute-missing deep graph clustering (DGC), which groups graph data containing samples with missing attributes, holds great potential to address the challenges of label scarcity and data absence in real-world scenarios. Among existing efforts, imputation-driven solutions (e.g., AMGC) employ the idea of classical imp...