  Graph Convolutional Networks (GCNs) are powerful models for learning
representations of attributed graphs. To scale GCNs to large graphs,
state-of-the-art methods use various layer sampling techniques to alleviate the
"neighbor explosion" problem during minibatch training. We propose GraphSAINT,
a graph sampling based inductive learning method that improves training
efficiency and accuracy in a fundamentally different way. By changing
perspective, GraphSAINT constructs minibatches by sampling the training graph,
rather than the nodes or edges across GCN layers. Each iteration, a complete
GCN is built from the properly sampled subgraph. Thus, we ensure fixed number
of well-connected nodes in all layers. We further propose normalization
technique to eliminate bias, and sampling algorithms for variance reduction.
Importantly, we can decouple the sampling from the forward and backward
propagation, and extend GraphSAINT with many architecture variants (e.g., graph
attention, jumping connection). GraphSAINT demonstrates superior performance in
both accuracy and training time on five large graphs, and achieves new
state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).
