  Node classifiers are required to comprehensively reduce prediction errors,
training resources, and inference latency in the industry. However, most graph
neural networks (GNN) concentrate only on one or two of them. The compromised
aspects thus are the shortest boards on the bucket, hindering their practical
deployments for industrial-level tasks. This work proposes a novel
semi-supervised learning method termed Graph Entropy Minimization (GEM) to
resolve the three issues simultaneously. GEM benefits its one-hop aggregation
from massive uncategorized nodes, making its prediction accuracy comparable to
GNNs with two or more hops message passing. It can be decomposed to support
stochastic training with mini-batches of independent edge samples, achieving
extremely fast sampling and space-saving training. While its one-hop
aggregation is faster in inference than deep GNNs, GEM can be further
accelerated to an extreme by deriving a non-hop classifier via online knowledge
distillation. Thus, GEM can be a handy choice for latency-restricted and
error-sensitive services running on resource-constraint hardware. Code is
available at https://github.com/cf020031308/GEM.
