5 papers
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…
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…
Spectral Subspace Clustering for Attributed Graphs
Xiaoyang Lin, Renchi Yang, Haoran Zheng +1
Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domai…
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…
Effective Clustering on Large Attributed Bipartite Graphs
Renchi Yang, Yidu Wu, Xiaoyang Lin +3
Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, su…