Graph neural networks (GNN) have recently emerged as a vehicle for applying
deep network architectures to graph and relational data. However, given the
increasing size of industrial datasets, in many practical situations the
message passing computations required for sharing information across GNN layers
are no longer scalable. Although various sampling methods have been introduced
to approximate full-graph training within a tractable budget, there remain
unresolved complications such as high variances and limited theoretical
guarantees. To address these issues, we build upon existing work and treat GNN
neighbor sampling as a multi-armed bandit problem but with a newly-designed
reward function that introduces some degree of bias designed to reduce variance
and avoid unstable, possibly-unbounded pay outs. And unlike prior bandit-GNN
use cases, the resulting policy leads to near-optimal regret while accounting
for the GNN training dynamics introduced by SGD. From a practical standpoint,
this translates into lower variance estimates and competitive or superior test
accuracy across several benchmarks.

在处理大型工业数据集时，考虑到 GNN 层之间的信息共享会导致大量的消息传递计算，为了解决高方差的问题，可以将 GNN 邻域抽样看作是一个多臂赌博机问题，并设计了一个新的奖励函数，可将一定程度的偏差引入样本采样中，以减少方差并避免不稳定的、可能无界的支出。