Graph Attention Multi-Layer Perceptron

Understand

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 under industrial scenarios.
• Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed $K$-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions.
• To tackle the above issue, we propose a new GNN architecture -- Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge.