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Multi-relational graph is a ubiquitous and important data structure, allowingflexible representation of multiple types of interactions and relations betweenentities. Similar to other graph-structured data, link prediction is one of themost important tasks on multi-relational graphs and is often used for knowledgecompletion. When related graphs coexist, it is of great benefit to build alarger graph via integrating the smaller ones. The integration requirespredicting hidden relational connections between entities belonged to differentgraphs (inter-domain link prediction). However, this poses a real challenge toexisting methods that are exclusively designed for link prediction betweenentities of the same graph only (intra-domain link prediction). In this study,we propose a new approach to tackle the inter-domain link prediction problem bysoftly aligning the entity distributions between different domains with optimaltransport and maximum mean discrepancy regularizers. Experiments on real-worlddatasets show that optimal transport regularizer is beneficial and considerablyimproves the performance of baseline methods.

Inter-domain Multi-relational Link Prediction