Developing scalable solutions for training Graph Neural Networks (GNNs) for
link prediction tasks is challenging due to the high data dependencies which
entail high computational cost and huge memory footprint. We propose a new
method for scaling training of knowledge graph embedding models for link
prediction to address these challenges. Towards this end, we propose the
following algorithmic strategies: self-sufficient partitions, constraint-based
negative sampling, and edge mini-batch training. Both, partitioning strategy
and constraint-based negative sampling, avoid cross partition data transfer
during training. In our experimental evaluation, we show that our scaling
solution for GNN-based knowledge graph embedding models achieves a 16x speed up
on benchmark datasets while maintaining a comparable model performance as
non-distributed methods on standard metrics.

为训练图神经网络中的知识图嵌入模型提供可扩展解决方案，其中算法策略包括独立分区、基于约束的负样本采样和边缘小批量训练，以避免跨分区数据传输，具有 16 倍的加速效果，而模型性能与非分布式方法相当。