  Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph
Completion (KGC) by modelling how entities and relations interact in recent
years. However, the explanation of the predicted facts has not caught the
necessary attention. Proper explanations for the results of GNN-based KGC
models increase model transparency and help researchers develop more reliable
models. Existing practices for explaining KGC tasks rely on
instance/subgraph-based approaches, while in some scenarios, paths can provide
more user-friendly and interpretable explanations. Nonetheless, the methods for
generating path-based explanations for KGs have not been well-explored. To
address this gap, we propose Power-Link, the first path-based KGC explainer
that explores GNN-based models. We design a novel simplified graph-powering
technique, which enables the generation of path-based explanations with a fully
parallelisable and memory-efficient training scheme. We further introduce three
new metrics for quantitative evaluation of the explanations, together with a
qualitative human evaluation. Extensive experiments demonstrate that Power-Link
outperforms the SOTA baselines in interpretability, efficiency, and
scalability.
