3 papers
cs.LG2025
Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering
Songbo Wang, Renchi Yang, Yurui Lai +2
The emergence of graph neural networks (GNNs) has offered a powerful tool for semi-supervised node classification tasks. Subsequent studies have achieved further improvements throu…
cs.LG2025
Simple yet Effective Graph Distillation via Clustering
Yurui Lai, Taiyan Zhang, Renchi Yang
Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to…
cs.LG2024
Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
Taiyan Zhang, Renchi Yang, Yurui Lai +3
Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes.…