Stochastic Training of Graph Convolutional Networks with Variance Reduction

TL;DR

This paper introduces variance reduction algorithms for training GCNs with minimal neighbor sampling, providing convergence guarantees and significantly improving runtime efficiency on large datasets.

Contribution

It develops control variate based algorithms that enable small neighbor sampling in GCNs with proven convergence guarantees, a novel approach in the field.

Findings

Achieves convergence with only two neighbors per node.

Reduces runtime to one seventh of previous methods on Reddit dataset.

Maintains similar convergence speed as exact algorithms.

Abstract

Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds. In this paper, we develop control variate based algorithms which allow sampling an arbitrarily small neighbor size. Furthermore, we prove new theoretical guarantee for our algorithms to converge to a local optimum of GCN. Empirical results show that our algorithms enjoy a similar convergence with the exact algorithm using only two neighbors per node. The runtime of our algorithms on a large Reddit dataset is only one seventh of previous neighbor sampling algorithms.