78 citations · 93 across the 7 of their papers we have counts for
8 papers
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Yige Yuan, Bingbing Xu, Huawei Shen +4
Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (Info…
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network
Yang Li, Bingbing Xu, Qi Cao +2
Improving the scalability of GNNs is critical for large graphs. Existing methods leverage three sampling paradigms including node-wise, layer-wise and subgraph sampling, then desig…
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification
Zhaohui Wang, Qi Cao, Huawei Shen +2
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we propose a novel graph isomorphism t…
Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network
Yuchen Fang, Yanjun Qin, Haiyong Luo +4
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate tempora…
Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning
Bingbing Xu, Huawei Shen, Qi Cao +2
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data. The key to graph-based semisupervised learning is capturing the smoothnes…
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
Bingbing Xu, Junjie Huang, Liang Hou +3
Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the represe…