论文标题
纽伯层采样 - 切碎GNN的邻里爆炸
Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
论文作者
论文摘要
Graph神经网络（GNN）最近受到了极大的关注，但是大规模培训它们仍然是一个挑战。迷你批次培训加上抽样的培训可缓解这一挑战。但是，现有方法要么患有邻里爆炸现象，要么表现较差。为了解决这些问题，我们提出了一种新的抽样算法，称为层邻内伯林采样（劳动）。它被设计为对邻居采样（NS）的直接替换，并具有相同的扇形高参数，同时采样了高达7倍的顶点，而无需牺牲质量。根据设计，每个顶点的估计器的方差从单个顶点的角度匹配NS。此外，在相同的顶点采样预算限制下，劳动收敛的速度比现有的层采样方法快，与NS相比，批次大小最多可以使用112倍。
Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used to alleviate this challenge. However, existing approaches either suffer from the neighborhood explosion phenomenon or have poor performance. To address these issues, we propose a new sampling algorithm called LAyer-neighBOR sampling (LABOR). It is designed to be a direct replacement for Neighbor Sampling (NS) with the same fanout hyperparameter while sampling up to 7 times fewer vertices, without sacrificing quality. By design, the variance of the estimator of each vertex matches NS from the point of view of a single vertex. Moreover, under the same vertex sampling budget constraints, LABOR converges faster than existing layer sampling approaches and can use up to 112 times larger batch sizes compared to NS.
