Cost-Efficient and Scalable Distributed Training for Large-Scale Graph Neural Networks

CATGNN is a cost-efficient and scalable distributed training system for graph neural networks that can handle billion-scale or larger graphs under limited computational resources by leveraging streaming-based graph partitioning and model averaging for synchronization.