5 papers
Semi-Supervised Text-Attributed Graph Distillation
Yurui Lai, Samir Moustafa, Renchi Yang +1
{\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing representation learning methods ov…
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…
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…
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.…
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
Yurui Lai, Xiaoyang Lin, Renchi Yang +1
In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs…