  While hyper-parameters (HPs) are important for knowledge graph (KG) learning,
existing methods fail to search them efficiently. To solve this problem, we
first analyze the properties of different HPs and measure the transfer ability
from small subgraph to the full graph. Based on the analysis, we propose an
efficient two-stage search algorithm KGTuner, which efficiently explores HP
configurations on small subgraph at the first stage and transfers the
top-performed configurations for fine-tuning on the large full graph at the
second stage. Experiments show that our method can consistently find better HPs
than the baseline algorithms within the same time budget, which achieves
{9.1\%} average relative improvement for four embedding models on the
large-scale KGs in open graph benchmark.
