GraphSAINT: Graph Sampling Based Inductive Learning Method

TL;DR

GraphSAINT introduces a novel graph sampling method for inductive learning that constructs minibatches from subgraphs, improving training efficiency and accuracy for large attributed graphs.

Contribution

It presents a new sampling approach that constructs complete GCNs from subgraphs, decoupling sampling from propagation, and extends to various architectures.

Findings

Achieves state-of-the-art F1 scores on PPI and Reddit datasets.

Demonstrates superior accuracy and training speed on large graphs.

Supports architecture extensions like attention and jumping connections.

Abstract

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).