112 citations · 217 across the 8 of their papers we have counts for
7 papers · 1 filter
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang +4
Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. Howeve…
Model Degradation Hinders Deep Graph Neural Networks
Wentao Zhang, Zeang Sheng, Ziqi Yin +5
Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks.However, drastic performance degradation is always observed when a GNN is stacked with many l…
Graph Attention Multi-Layer Perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng +6
Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications un…
Node Dependent Local Smoothing for Scalable Graph Learning
Wentao Zhang, Mingyu Yang, Zeang Sheng +5
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs). Concretely, they show feature smoothing combined with simple linear regression…
Evaluating Deep Graph Neural Networks
Wentao Zhang, Zeang Sheng, Yuezihan Jiang +4
Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment t…
Graph Attention MLP with Reliable Label Utilization
Wentao Zhang, Ziqi Yin, Zeang Sheng +5
Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications. Despite the high expressive power, they typically need to perform…
ROD: Reception-aware Online Distillation for Sparse Graphs
Wentao Zhang, Yuezihan Jiang, Yang Li +6
Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance…