Before revision:  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.
After revision:  Although some work has explored training on large-scale graphs (e.g., GraphSAGE, ClusterGCN, etc.), we pioneer efficient training of wide (overparameterized) GCNs on large graphs.