  Inductive relation reasoning for knowledge graphs, aiming to infer missing
links between brand-new entities, has drawn increasing attention. The models
developed based on Graph Inductive Learning, called GraIL-based models, have
shown promising potential for this task. However, the uni-directional
message-passing mechanism hinders such models from exploiting hidden mutual
relations between entities in directed graphs. Besides, the enclosing subgraph
extraction in most GraIL-based models restricts the model from extracting
enough discriminative information for reasoning. Consequently, the expressive
ability of these models is limited. To address the problems, we propose a novel
GraIL-based inductive relation reasoning model, termed MINES, by introducing a
Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph.
Concretely, the message intercommunication mechanism is designed to capture the
omitted hidden mutual information. It introduces bi-directed information
interactions between connected entities by inserting an undirected/bi-directed
GCN layer between uni-directed RGCN layers. Moreover, inspired by the success
of involving more neighbors in other graph-based tasks, we extend the
neighborhood area beyond the enclosing subgraph to enhance the information
collection for inductive relation reasoning. Extensive experiments on twelve
inductive benchmark datasets demonstrate that our MINES outperforms existing
state-of-the-art models, and show the effectiveness of our intercommunication
mechanism and reasoning on the neighbor-enhanced subgraph.
