684 citations · 1.2k across the 12 of their papers we have counts for
12 papers
Causal invariant geographic network representations with feature and structural distribution shifts
Yuhan Wang, Silu He, Qinyao Luo +4
The existing methods learn geographic network representations through deep graph neural networks (GNNs) based on the i.i.d. assumption. However, the spatial heterogeneity and tempo…
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…
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
Haifeng Li, Jun Cao, Jiawei Zhu +3
The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and…
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…
KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu, Xin Han, Hanhan Deng +5
While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic…
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…
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Jie Chen, Ziyang Yuan, Jian Peng +5
Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change i…
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
Jie Chen, Haozhe Huang, Jian Peng +5
Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutio…
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
Enqiang Guo, Xinsha Fu, Jiawei Zhu +4
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to…