  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.
