GraphSAINT: Graph Sampling Based Inductive Learning Method

Understand

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.