Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport

Published in IEEE DSAA 2020 (accepted rate 28.3%), 2020

Recommended citation: Luu Huu Phuc, Koh Takeuchi, Makoto Yamada, Hisashi Kashima. IEEE International Conference on Data Science and Advanced Analytics. IEEE DSAA 2020.

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Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. When related graphs coexist, it is of great benefit to build a larger graph via integrating the smaller ones. The integration requires predicting hidden relational connections between entities belonged to different graphs (inter-domain link prediction). However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities 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 by softly aligning the entity distributions between different domains with optimal transport and maximum mean discrepancy regularizers. Experiments on real-world datasets show that optimal transport regularizer is beneficial and considerably improves the performance of baseline methods.