38 citations · 56 across the 12 of their papers we have counts for
4 papers · 1 filter
Prioritized Propagation in Graph Neural Networks
Yao Cheng, Minjie Chen, Xiang Li +2
Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different n…
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Yige Zhao, Jianxiang Yu, Yao Cheng +4
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existi…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…
Graph Self-Contrast Representation Learning
Minjie Chen, Yao Cheng, Ye Wang +2
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one posi…