Graph neural networks have been shown successful in recent years. While different GNN architectures and training systems have been developed, GNN training on large-scale real-world graphs still remains challenging. Existing distributed systems load the entire graph in memory for graph partitioning, requiring a huge memory space to process large graphs and thus hindering GNN training on such large graphs using commodity workstations. In this paper, we propose CATGNN, a cost-efficient and scalable distributed GNN training system which focuses on scaling GNN training to billion-scale or larger graphs under limited computational resources. Among other features, it takes a stream of edges as input, instead of loading the entire graph in memory, for partitioning. We also propose a novel streaming partitioning algorithm named SPRING for distributed GNN training. We verify the correctness and effectiveness of CATGNN with SPRING on 16 open datasets. In particular, we demonstrate that CATGNN can handle the largest publicly available dataset with limited memory, which would have been infeasible without increasing the memory space. SPRING also outperforms state-of-the-art partitioning algorithms significantly, with a 50% reduction in replication factor on average.

CATGNN是一个成本效益高且可扩展的分布式GNN训练系统，专注于在有限计算资源下扩展GNN训练到十亿规模或更大规模的图，并提出了一种名为SPRING的新型流式分区算法来进行分布式GNN训练。在16个开放数据集上验证了CATGNN与SPRING的正确性和有效性，尤其是证明了CATGNN能够处理最大的公开可用数据集，并在平均复制因子上显著优于最先进的分区算法50%。

CATGNN: 图神经网络的高效可扩展分布式训练