6 citations · 11 across the 5 of their papers we have counts for
5 papers
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
Guangji Bai, Ziyang Yu, Zheng Chai +2
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computin…
Deep Spatial Domain Generalization
Dazhou Yu, Guangji Bai, Yun Li +1
Spatial autocorrelation and spatial heterogeneity widely exist in spatial data, which make the traditional machine learning model perform badly. Spatial domain generalization is a…
An Invertible Graph Diffusion Neural Network for Source Localization
Junxiang Wang, Junji Jiang, Liang Zhao
Localizing the source of graph diffusion phenomena, such as misinformation propagation, is an important yet extremely challenging task. Existing source localization models typicall…
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks
Guangji Bai, Chen Ling, Liang Zhao
Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen…
Heterogeneous Temporal Graph Neural Network
Yujie Fan, Mingxuan Ju, Chuxu Zhang +2
Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spa…