167 citations · 526 across the 20 of their papers we have counts for
4 papers · 1 filter
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Weigang Lu, Ziyu Guan, Wei Zhao +5
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions…
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Yaming Yang, Ziyu Guan, Zhe Wang +4
Recent self-supervised pre-training methods on Heterogeneous Information Networks (HINs) have shown promising competitiveness over traditional semi-supervised Heterogeneous Graph N…
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Weigang Lu, Yibing Zhan, Binbin Lin +6
Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing…
Interpretable and Efficient Heterogeneous Graph Convolutional Network
Yaming Yang, Ziyu Guan, Jianxin Li +3
Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Inform…