LRHNE: A Latent-Relation Enhanced Embedding Method for Heterogeneous Information Networks

Abstract

Heterogeneous information networks (HINs) have been successfully applied into several fields to accomplish complex data analytics, such as bibliography, bioinformatics, NLP, etc. In the meantime, network embedding at present has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. Despite recent breakthroughs in HIN embedding methods, little research attention has been paid to exploit the relation semantics in HINs and further integrate it to improve the embedding quality. Considering the sophisticated correlations in HINs, we in this paper propose a novel HIN embedding method LRHNE to yield latent-relation enhanced embeddings for nodes. Our work mainly involves three contributions: i) we verify that the latent relation can promote the embedding quality indeed through a real-world dataset, then a novel graph inception network is proposed to extract the latent relational features under the guidance of partial prior knowledge; ii) taking into account the existing structure information and inferred latent relation knowledge, we propose a cross-aligned variational graph autoencoder to extract and further fuse both the structure and latent relational features into the embeddings; and iii) we perform extensive experiments to validate our proposed LRHNE, and experimental results show that our LRHNE can significantly outperform state-of-the-art methods. The multi-facet inspections also exhibit our method is robust and hyper-parameter insensitive, therefore, our method can serve as a radical tool to tackle the relation-sophisticated HINs.