The paper creates Adam-style optimizers that combine neighbor sampling and control variates for graph neural networks, with optimal convergence rates and better node-classification performance than control-variate SGD.
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Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
The paper creates Adam-style optimizers that combine neighbor sampling and control variates for graph neural networks, with optimal convergence rates and better node-classification performance than control-variate SGD.