GNN Training Framework

Training Graph Neural Networks (GNNs) on massive graphs presents significant computational challenges, driving research into efficient distributed training frameworks. Current efforts focus on minimizing communication overhead through techniques like asynchronous updates, one-bit quantization, and novel graph partitioning strategies (e.g., vertex cut), alongside methods to mitigate the staleness of distributed information. These advancements aim to improve both the speed and scalability of GNN training, enabling the application of these powerful models to increasingly large and complex datasets in various domains, such as social network analysis and drug discovery.