Before revision:  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 .
After revision:  This distributed framework improves model performance , scales to training on arbitrarily large graphs, 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 .