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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
Effective Clustering for Large Multi-Relational Graphs
Xiaoyang Lin, Runhao Jiang, Renchi Yang
Multi-relational graphs (MRGs) are an expressive data structure for modeling diverse interactions/relations among real objects (i.e., nodes), which pervade extensive applications a…
cs.LG2024
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…