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Graph Neural Networks (GNNs) are a new and increasingly popular family ofdeep neural network architectures to perform learning on graphs. Training themefficiently is challenging due to the irregular nature of graph data. Theproblem becomes even more challenging when scaling to large graphs that exceedthe capacity of single devices. Standard approaches to distributed DNNtraining, such as data and model parallelism, do not directly apply to GNNs.Instead, two different approaches have emerged in the literature: whole-graphand sample-based training.  In this paper, we review and compare the two approaches. Scalability ischallenging with both approaches, but we make a case that research should focuson sample-based training since it is a more promising approach. Finally, wereview recent systems supporting sample-based training.

Scalable Graph Neural Network Training: The Case for Sampling