(LA)yer-neigh(BOR) Sampling: Defusing Neighborhood Explosion in GNNs
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Graph Neural Networks have recently received a significant attention, however, training them at a large scale still remains a challenge. Minibatch training coupled with sampling is used to alleviate this challenge. Even so existing approaches either suffer from the neighborhood explosion phenomenon or do not have good performance. To deal with these issues, we propose a new sampling algorithm called LAyer-neighBOR sampling (LABOR). It is designed to be a direct replacement for Neighborhood Sampling with the same fanout hyperparameter while sampling much fewer vertices, without sacrificing quality. By design, the variance of the estimator of each vertex matches Neighbor Sampling from the point of view of a single vertex. In our experiments, we demonstrate the superiority of our approach when it comes to model convergence behaviour against Neighbor Sampling and also the other Layer Sampling approaches under the same limited vertex sampling budget constraints.
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