Scalable Graph Neural Networks via Bidirectional Propagation

Graph Neural Networks (GNN) are an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use graph sampling or layer-wise sampling techniques to reduce training time; However, these methods still suffer from degrading performance and scalability problems when applying to graphs with billions of edges.