HINE: Heterogeneous Information Network Embedding

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10177)

Abstract

Network embedding has shown its effectiveness in embedding homogeneous networks. Compared with homogeneous networks, heterogeneous information networks (HINs) contain semantic information from multi-typed entities and relations, and are shown to be a more effective model for real world data. The existing network embedding methods fail to explicitly capture the semantics in HINs. In this paper, we propose an HIN embedding model (HINE), which consists of local and global semantic embedding. Local semantic embedding aims to incorporate entity type information via embedding the local structures and types of the entities in a supervised way. Global semantic embedding leverages multi-hop relation types among entities to propagate the global semantics via a Markov Random Field (MRF) to impact the embedding vectors. By doing so, HINE is capable to capture both local and global semantic information in the embedding vectors. Experimental results show that HINE significantly outperforms state-of-the-art methods.

Keywords

Heterogeneous information network Network embedding Semantic embedding 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of High Confidence Software Technologies (Ministry of Education)EECS, Peking UniversityBeijingChina
  2. 2.IBM Research AlmadenSan JoseUSA

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