Scalable Graph Neural Networks via Bidirectional Propagation

Understand

Graph Neural Networks (GNN) is 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.