5 papers · 1 filter
Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filtering
Haoran Zheng, Renchi Yang, Hongtao Wang +1
Multimodal Attributed Graphs (MMAGs) are an expressive data model for representing the complex interconnections among entities that associate attributes from multiple data modaliti…
Rethinking Message Passing Neural Networks with Diffusion Distance-guided Stress Majorization
Haoran Zheng, Renchi Yang, Yubo Zhou +1
Message passing neural networks (MPNNs) have emerged as go-to models for learning on graph-structured data in the past decade. Despite their effectiveness, most of such models stil…
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.…