  Inductive knowledge graph completion (KGC) aims to infer the missing relation
for a set of newly-coming entities that never appeared in the training set.
Such a setting is more in line with reality, as real-world KGs are constantly
evolving and introducing new knowledge. Recent studies have shown promising
results using message passing over subgraphs to embed newly-coming entities for
inductive KGC. However, the inductive capability of these methods is usually
limited by two key issues. (i) KGC always suffers from data sparsity, and the
situation is even exacerbated in inductive KGC where new entities often have
few or no connections to the original KG. (ii) Cold-start problem. It is over
coarse-grained for accurate KG reasoning to generate representations for new
entities by gathering the local information from few neighbors. To this end, we
propose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KG
completion. It aims to mine latent relation patterns for inductive KG
completion. Specifically, by centering on relations, NORAN provides a hyper
view towards KG modeling, where the correlations between relations can be
naturally captured as entity-independent logical evidence to conduct inductive
KGC. Extensive experiment results on five benchmarks show that our framework
substantially outperforms the state-of-the-art KGC methods.
