Jieren Cheng

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...

Anchor-Driven Nystr¨om for Deep Graph-Level Clustering

Mutually Reinforced Fair Multi-View Clustering

The proposed MR-FMVC optimizes fairness alignment and clustering iteratively to enable coordinated optimization and achieves a superior trade-off be-tween clustering performance and fairness.

FedIGL: Federated Invariant Graph Learning for Non-IID Graphs

This work proposes a FedIGL framework based on invariant learning, which effectively disrupts spurious correlations and further mines the invariant factors across different distributions, and proposes a novel Bi-Gradient Regularization strategy that introduces gradient constraints to guide the model in identifying clie...