Random node sampling in GNN mini-batches implicitly minimizes sampled loss plus a gradient-variance regularizer, yielding performance equal or superior to full-graph training on most datasets.
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Implicit Regularization of Mini-Batch Training in Graph Neural Networks
Random node sampling in GNN mini-batches implicitly minimizes sampled loss plus a gradient-variance regularizer, yielding performance equal or superior to full-graph training on most datasets.