The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale in terms of both the size of the graph and the number of model parameters. These limitations are in stark contrast to the increasing scale (in data size and model size) of experiments in deep learning research. In this work, we propose GIST, a novel distributed approach that enables efficient training of wide (overparameterized) GCNs on large graphs. GIST is a hybrid layer and graph sampling method, which disjointly partitions the global model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel. This distributed framework improves model performance and significantly decreases wall-clock training time. GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning.

翻译：图形革命网络(GCN)是图中机器学习的一流解决方案,但其培训在图形大小和模型参数数量两方面都很难进行,与深层学习研究实验规模的扩大(数据大小和模型大小)形成鲜明对比。在这项工作中,我们建议GIST是一种新颖的分布式方法,能够对大图上的广度(超光度)GCN进行有效培训。GIST是一种混合层和图表取样方法,它将全球模型分解成若干小的子GCN,在多个GPU之间同时进行独立培训。这个分布式框架提高了模型性能,大大缩短了单时钟培训时间。GIST力求使大型GCN实验成为缩小图形机学习和深层学习之间现有差距的目标。