  Knowledge graph completion (KGC) is one of the effective methods to identify
new facts in knowledge graph. Except for a few methods based on graph network,
most of KGC methods trend to be trained based on independent triples, while are
difficult to take a full account of the information of global network
connection contained in knowledge network. To address these issues, in this
study, we propose a simple and effective Network-based Pre-training framework
for knowledge graph completion (termed NetPeace), which takes into account the
information of global network connection and local triple relationships in
knowledge graph. Experiments show that in NetPeace framework, multiple KGC
models yields consistent and significant improvements on benchmarks (e.g.,
36.45% Hits@1 and 27.40% MRR improvements for TuckER on FB15k-237), especially
dense knowledge graph. On the challenging low-resource task, NetPeace that
benefits from the global features of KG achieves higher performance (104.03%
MRR and 143.89% Hit@1 improvements at most) than original models.
