  Training multi-layer Graph Convolution Networks (GCN) using standard SGD
techniques scales poorly as each descent step ends up updating node embeddings
for a large portion of the graph. Recent attempts to remedy this sub-sample the
graph that reduces compute but introduce additional variance and may offer
suboptimal performance. This paper develops the IGLU method that caches
intermediate computations at various GCN layers thus enabling lazy updates that
significantly reduce the compute cost of descent. IGLU introduces bounded bias
into the gradients but nevertheless converges to a first-order saddle point
under standard assumptions such as objective smoothness. Benchmark experiments
show that IGLU offers up to 1.2% better accuracy despite requiring up to 88%
less compute.
