  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 an expensive recursive neighborhood
expansion in multiple training epochs and face a scalability issue. Moreover,
most of them are inflexible since they are restricted to fixed-hop
neighborhoods and insensitive to actual receptive field demands for different
nodes. We circumvent these limitations by introducing a scalable and flexible
Graph Attention Multilayer Perceptron (GAMLP). With the separation of the
non-linear transformation and feature propagation, GAMLP significantly improves
the scalability and efficiency by performing the propagation procedure in a
pre-compute manner. With three principled receptive field attention, each node
in GAMLP is flexible and adaptive in leveraging the propagated features over
the different sizes of reception field. We conduct extensive evaluations on the
three large open graph benchmarks (e.g., ogbn-papers100M, ogbn-products and
ogbn-mag), demonstrating that GAMLP not only achieves the state-of-art
performance, but also additionally provide high scalability and efficiency.
