98 citations · 152 across the 7 of their papers we have counts for
9 papers
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples
Jiawei Zhu, Mei Hong, Ronghua Du +1
In recent years, using a self-supervised learning framework to learn the general characteristics of graphs has been considered a promising paradigm for graph representation learnin…
Curvature Graph Neural Network
Haifeng Li, Jun Cao, Jiawei Zhu +3
Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables meas…
Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
Haifeng Li, Jun Cao, Jiawei Zhu +2
One of the key problems of GNNs is how to describe the importance of neighbor nodes in the aggregation process for learning node representations. A class of GNNs solves this proble…
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu, Chao Tao, Hanhan Deng +4
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow informati…
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
Haifeng Li, Zhenqi Cui, Zhiqing Zhu +4
Training a modern deep neural network on massive labeled samples is the main paradigm in solving the scene classification problem for remote sensing, but learning from only a few d…
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu, Yujiao Song, Ling Zhao +1
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging consider…