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GraphSAINT: Graph Sampling Based Inductive Learning Method

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arxiv 1907.04931 v4 pith:RDGXBKNO submitted 2019-07-10 cs.LG stat.ML

Hanqing Zeng , Hongkuan Zhou , Ajitesh Srivastava , Rajgopal Kannan , Viktor Prasanna This is my paper
classification cs.LGstat.ML
keywords samplinggraphgraphsainttraininggraphslearningaccuracygcns
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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).

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Cited by 11 Pith papers

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    FuseSampleAgg performs neighbor sampling and mean aggregation for 1-2 hop GraphSAGE in a single fused CUDA pass, eliminating intermediate block tensors and reporting 1.1-51x step-time and 3.4-100x peak-memory gains ov...

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