402 citations · 466 across the 3 of their papers we have counts for
9 papers
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…
Personalized PageRank to a Target Node, Revisited
Hanzhi Wang, Zhewei Wei, Junhao Gan +2
Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node and a target node , the PPR value repr…
SCE: Scalable Network Embedding from Sparsest Cut
Shengzhong Zhang, Zengfeng Huang, Haicang Zhou +1
Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the unde…
Ghost imaging based on Y-net: a dynamic coding and conjugate-decoding approach
Ruiguo Zhu, Hong Yu, Zhijie Tan +4
Ghost imaging incorporating deep learning technology has recently attracted much attention in the optical imaging field. However, deterministic illumination and multiple exposure a…
Higher-order Weighted Graph Convolutional Networks
Songtao Liu, Lingwei Chen, Hanze Dong +3
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neig…
Optimization of light fields in ghost imaging using dictionary learning
Chenyu Hu, Zhisheng Tong, Zhentao Liu +3
Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often…