Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs

Understand

Graph neural networks (GNNs) are powerful tools for learning from graph data and are widely used in various applications such as social network recommendation, fraud detection, and graph search.

• The graphs in these applications are typically large, usually containing hundreds of millions of nodes.
• Training GNN models on such large graphs efficiently remains a big challenge.
• Despite a number of sampling-based methods have been proposed to enable mini-batch training on large graphs, these methods have not been proved to work on truly industry-scale graphs, which require GPUs or mixed-CPU-GPU training.