  Graph Neural Networks (GNNs) are popular for graph machine learning and have
shown great results on wide node classification tasks. Yet, they are less
popular for practical deployments in the industry owing to their scalability
challenges incurred by data dependency. Namely, GNN inference depends on
neighbor nodes multiple hops away from the target, and fetching them burdens
latency-constrained applications. Existing inference acceleration methods like
pruning and quantization can speed up GNNs by reducing
Multiplication-and-ACcumulation (MAC) operations, but the improvements are
limited given the data dependency is not resolved. Conversely, multi-layer
perceptrons (MLPs) have no graph dependency and infer much faster than GNNs,
even though they are less accurate than GNNs for node classification in
general. Motivated by these complementary strengths and weaknesses, we bring
GNNs and MLPs together via knowledge distillation (KD). Our work shows that the
performance of MLPs can be improved by large margins with GNN KD. We call the
distilled MLPs Graph-less Neural Networks (GLNNs) as they have no inference
graph dependency. We show that GLNNs with competitive accuracy infer faster
than GNNs by 146X-273X and faster than other acceleration methods by 14X-27X.
Under a production setting involving both transductive and inductive
predictions across 7 datasets, GLNN accuracies improve over stand-alone MLPs by
12.36% on average and match GNNs on 6/7 datasets. Comprehensive analysis shows
when and why GLNNs can achieve competitive accuracies to GNNs and suggests GLNN
as a handy choice for latency-constrained applications.
