  Graph representation learning has recently been applied to a broad spectrum
of problems ranging from computer graphics and chemistry to high energy physics
and social media. The popularity of graph neural networks has sparked interest,
both in academia and in industry, in developing methods that scale to very
large graphs such as Facebook or Twitter social networks. In most of these
approaches, the computational cost is alleviated by a sampling strategy
retaining a subset of node neighbors or subgraphs at training time. In this
paper we propose a new, efficient and scalable graph deep learning architecture
which sidesteps the need for graph sampling by using graph convolutional
filters of different size that are amenable to efficient precomputation,
allowing extremely fast training and inference. Our architecture allows using
different local graph operators (e.g. motif-induced adjacency matrices or
Personalized Page Rank diffusion matrix) to best suit the task at hand. We
conduct extensive experimental evaluation on various open benchmarks and show
that our approach is competitive with other state-of-the-art architectures,
while requiring a fraction of the training and inference time. Moreover, we
obtain state-of-the-art results on ogbn-papers100M, the largest public graph
dataset, with over 110 million nodes and 1.5 billion edges.
