  Graph neural networks (GNNs) have been widely adopted for semi-supervised
learning on graphs. A recent study shows that the graph random neural network
(GRAND) model can generate state-of-the-art performance for this problem.
However, it is difficult for GRAND to handle large-scale graphs since its
effectiveness relies on computationally expensive data augmentation procedures.
In this work, we present a scalable and high-performance GNN framework GRAND+
for semi-supervised graph learning. To address the above issue, we develop a
generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general
propagation matrix and employ it to perform graph data augmentation in a
mini-batch manner. We show that both the low time and space complexities of
GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we
introduce a confidence-aware consistency loss into the model optimization of
GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive
experiments on seven public datasets of different sizes. The results
demonstrate that GRAND+ 1) is able to scale to large graphs and costs less
running time than existing scalable GNNs, and 2) can offer consistent accuracy
improvements over both full-batch and scalable GNNs across all datasets.
