  Inductive link prediction for knowledge graph aims at predicting missing
links between unseen entities, those not shown in training stage. Most previous
works learn entity-specific embeddings of entities, which cannot handle unseen
entities. Recent several methods utilize enclosing subgraph to obtain inductive
ability. However, all these works only consider the enclosing part of subgraph
without complete neighboring relations, which leads to the issue that partial
neighboring relations are neglected, and sparse subgraphs are hard to be
handled. To address that, we propose Subgraph Neighboring Relations Infomax,
SNRI, which sufficiently exploits complete neighboring relations from two
aspects: neighboring relational feature for node feature and neighboring
relational path for sparse subgraph. To further model neighboring relations in
a global way, we innovatively apply mutual information (MI) maximization for
knowledge graph. Experiments show that SNRI outperforms existing state-of-art
methods by a large margin on inductive link prediction task, and verify the
effectiveness of exploring complete neighboring relations in a global way to
characterize node features and reason on sparse subgraphs.
