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Found 7 papers, 6 papers with code
Graph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based applications.
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Node Property Prediction
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Node Dependent Local Smoothing for Scalable Graph Learning
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
Model Degradation Hinders Deep Graph Neural Networks
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 layers.
Evaluating Deep Graph Neural Networks
Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs; (2) when we need and how to build deep GNNs.
ROD: Reception-aware Online Distillation for Sparse Graphs
Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.
Graph Attention MLP with Reliable Label Utilization
Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications.
