  In recent years, Graph Convolutional Networks (GCNs) have achieved great
success in learning from graph-structured data. With the growing tendency of
graph nodes and edges, GCN training by single processor cannot meet the demand
for time and memory, which led to a boom into distributed GCN training
frameworks research. However, existing distributed GCN training frameworks
require enormous communication costs between processors since multitudes of
dependent nodes and edges information need to be collected and transmitted for
GCN training from other processors. To address this issue, we propose a Graph
Augmentation based Distributed GCN framework(GAD). In particular, GAD has two
main components, GAD-Partition and GAD-Optimizer. We first propose a graph
augmentation-based partition (GAD-Partition) that can divide original graph
into augmented subgraphs to reduce communication by selecting and storing as
few significant nodes of other processors as possible while guaranteeing the
accuracy of the training. In addition, we further design a subgraph
variance-based importance calculation formula and propose a novel weighted
global consensus method, collectively referred to as GAD-Optimizer. This
optimizer adaptively reduces the importance of subgraphs with large variances
for the purpose of reducing the effect of extra variance introduced by
GAD-Partition on distributed GCN training. Extensive experiments on four
large-scale real-world datasets demonstrate that our framework significantly
reduces the communication overhead (50%), improves the convergence speed (2X)
of distributed GCN training, and slight gain in accuracy (0.45%) based on
minimal redundancy compared to the state-of-the-art methods.
