  Graph neural networks (GNNs) have emerged as a powerful approach for solving
many network mining tasks. However, learning on large graphs remains a
challenge - many recently proposed scalable GNN approaches rely on an expensive
message-passing procedure to propagate information through the graph. We
present the PPRGo model which utilizes an efficient approximation of
information diffusion in GNNs resulting in significant speed gains while
maintaining state-of-the-art prediction performance. In addition to being
faster, PPRGo is inherently scalable, and can be trivially parallelized for
large datasets like those found in industry settings. We demonstrate that PPRGo
outperforms baselines in both distributed and single-machine training
environments on a number of commonly used academic graphs. To better analyze
the scalability of large-scale graph learning methods, we introduce a novel
benchmark graph with 12.4 million nodes, 173 million edges, and 2.8 million
node features. We show that training PPRGo from scratch and predicting labels
for all nodes in this graph takes under 2 minutes on a single machine, far
outpacing other baselines on the same graph. We discuss the practical
application of PPRGo to solve large-scale node classification problems at
Google.
