  Graph Neural Networks (GNNs) have superior capability in learning graph data.
Full-graph GNN training generally has high accuracy, however, it suffers from
large peak memory usage and encounters the Out-of-Memory problem when handling
large graphs. To address this memory problem, a popular solution is mini-batch
GNN training. However, mini-batch GNN training increases the training variance
and sacrifices the model accuracy. In this paper, we propose a new
memory-efficient GNN training method using spanning subgraph, called SpanGNN.
SpanGNN trains GNN models over a sequence of spanning subgraphs, which are
constructed from empty structure. To overcome the excessive peak memory
consumption problem, SpanGNN selects a set of edges from the original graph to
incrementally update the spanning subgraph between every epoch. To ensure the
model accuracy, we introduce two types of edge sampling strategies (i.e.,
variance-reduced and noise-reduced), and help SpanGNN select high-quality edges
for the GNN learning. We conduct experiments with SpanGNN on widely used
datasets, demonstrating SpanGNN's advantages in the model performance and low
peak memory usage.
