论文标题
图形卷积网络的校准和DEBIAS层次采样
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
论文作者
论文摘要
已经开发出多种基于抽样的方法，用于近似和加速节点嵌入图卷积网络（GCN）训练中的聚合。其中，层层的方法递归地执行了重要性采样，以选择邻居共同为每一层中的现有节点共同选择邻居。本文从矩阵近似角度重新审视了该方法，并确定了现有层次采样方法中的两个问题：通过采样而无需替换而引起的次优采样概率和估计偏差。为了解决这些问题，我们因此提出了两种补救措施：构建抽样概率的新原则和有效的偏见算法。通过对估计方差和公共基准实验的广泛分析来证明这些改进。代码和算法实现可在https://github.com/ychen-stat-ml/gcn-layer-wise-smpling上公开获得。
Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer. This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement. To address these issues, we accordingly propose two remedies: a new principle for constructing sampling probabilities and an efficient debiasing algorithm. The improvements are demonstrated by extensive analyses of estimation variance and experiments on common benchmarks. Code and algorithm implementations are publicly available at https://github.com/ychen-stat-ml/GCN-layer-wise-sampling .
