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HINE: Heterogeneous Information Network Embedding

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Book cover Database Systems for Advanced Applications (DASFAA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

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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.

We are grateful to Tengjiao Wang for invaluable guidance, support and contribution in regard to this research and resulting paper. This research is supported by the Natural Science Foundation of China (Grant No. 61572043), and the National Key Research and Development Program (Grant No. 2016YFB1000704).

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Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies (Ministry of Education), EECS, Peking University, Beijing, China

    Yuxin Chen

  2. IBM Research Almaden, San Jose, California, USA

    Chenguang Wang

Authors
  1. Yuxin Chen
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  2. Chenguang Wang
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Correspondence to Yuxin Chen .

Editor information

Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Chen, Y., Wang, C. (2017). HINE: Heterogeneous Information Network Embedding. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-55753-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55752-6

  • Online ISBN: 978-3-319-55753-3

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