70 citations · 82 across the 4 of their papers we have counts for
4 papers · 1 filter
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons
Wei Huang, Yuan Cao, Haonan Wang +2
Graph neural networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data, often outperforming traditional Multilayer Perceptrons (MLPs) in nume…
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
Haonan Wang, Jieyu Zhang, Qi Zhu +3
Existing graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss, which is effective for capturing…
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Haonan Wang, Jieyu Zhang, Qi Zhu +1
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL…
Transferability of Spectral Graph Convolutional Neural Networks
Ron Levie, Wei Huang, Lorenzo Bucci +2
This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machin…