5 citations · 7 across the 4 of their papers we have counts for
5 papers
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
Yanbei Liu, Chongxu Wang, Zhitao Xiao +3
Heterogeneous Graph Neural Networks (HGNNs), have demonstrated excellent capabilities in processing heterogeneous information networks. Self-supervised learning on heterogeneous gr…
Multi-Scale Subgraph Contrastive Learning
Yanbei Liu, Yu Zhao, Xiao Wang +2
Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usu…
Independence Promoted Graph Disentangled Networks
Yanbei Liu, Xiao Wang, Shu Wu +1
We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node repres…
On the Performance of Massive MIMO Systems With Low-Resolution ADCs Over Rician Fading Channels
Tianle Liu, Jun Tong, Qinghua Guo +3
This paper considers uplink massive multiple-input multiple-output (MIMO) systems with lowresolution analog-to-digital converters (ADCs) over Rician fading channels. Maximum-ratio-…
Linear Shrinkage Estimation of Covariance Matrices Using Low-Complexity Cross-Validation
Jun Tong, Rui Hu, Jiangtao Xi +3
Shrinkage can effectively improve the condition number and accuracy of covariance matrix estimation, especially for low-sample-support applications with the number of training samp…