  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. We have deployed GAMLP in Tencent with the
Angel platform, and we further evaluate GAMLP on both real-world datasets and
large-scale industrial datasets. Extensive experiments on these 14 graph
datasets demonstrate that GAMLP achieves state-of-the-art performance while
enjoying high scalability and efficiency. Specifically, it outperforms GAT by
1.3\% regarding predictive accuracy on our large-scale Tencent Video dataset
while achieving up to $50\times$ training speedup. Besides, it ranks top-1 on
both the leaderboards of the largest homogeneous and heterogeneous graph (i.e.,
ogbn-papers100M and ogbn-mag) of Open Graph Benchmark.
