Renxiang Guan

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...

scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single‐Cell RNA‐seq

ABSTRACT Recent advancements in single‐cell RNA sequencing (scRNA‐seq) have greatly enhanced our ability to dissect cellular heterogeneity. However, unsupervised clustering often struggles to identify transitional or developmental boundary cells, as existing methods rely on highly variable genes without considering exp...

SCD-MVC: Stable Conditional Diffusion for Multi-view Clustering

SCD-MVC is proposed, a Stable Conditional Diffusion-driven framework for Multi-View Clustering that integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling.

SFKH-GC: Harnessing the Power of Full-Neighborhoods Information for Large-Scale Attributed-Graph Learning