  Recent advances in data processing have stimulated the demand for learning
graphs of very large scales. Graph Neural Networks (GNNs), being an emerging
and powerful approach in solving graph learning tasks, are known to be
difficult to scale up. Most scalable models apply node-based techniques in
simplifying the expensive graph message-passing propagation procedure of GNN.
However, we find such acceleration insufficient when applied to million- or
even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with
feature-oriented optimization for graph computation. SCARA efficiently computes
graph embedding from node features, and further selects and reuses feature
computation results to reduce overhead. Theoretical analysis indicates that our
model achieves sub-linear time complexity with a guaranteed precision in
propagation process as well as GNN training and inference. We conduct extensive
experiments on various datasets to evaluate the efficacy and efficiency of
SCARA. Performance comparison with baselines shows that SCARA can reach up to
100x graph propagation acceleration than current state-of-the-art methods with
fast convergence and comparable accuracy. Most notably, it is efficient to
process precomputation on the largest available billion-scale GNN dataset
Papers100M (111M nodes, 1.6B edges) in 100 seconds.
