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Adversarial Heterogeneous Network Embedding with Metapath Attention Mechanism

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Abstract

Heterogeneous information network (HIN)-structured data provide an effective model for practical purposes in real world. Network embedding is fundamental for supporting the network-based analysis and prediction tasks. Methods of network embedding that are currently popular normally fail to effectively preserve the semantics of HIN. In this study, we propose AGA2Vec, a generative adversarial model for HIN embedding that uses attention mechanisms and meta-paths. To capture the semantic information from multi-typed entities and relations in HIN, we develop a weighted meta-path strategy to preserve the proximity of HIN. We then use an autoencoder and a generative adversarial model to obtain robust representations of HIN. The results of experiments on several real-world datasets show that the proposed approach outperforms state-of-the-art approaches for HIN embedding.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, 200433, China

    Chun-Yang Ruan, Yanchun Zhang & Xin-Tian Chen

  2. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Chun-Yang Ruan, Yanchun Zhang & Xin-Tian Chen

  3. College of Computer Science, National University of Defense Technology, Changsha, 410073, China

    Ye Wang

  4. School of Science, Engineering and Information Technology, Federation University Australia, Melbourne, 3000, Australia

    Jiangang Ma

  5. College of Engineering and Science, Victoria University, Melbourne, 3000, Australia

    Yanchun Zhang

Authors
  1. Chun-Yang Ruan
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  2. Ye Wang
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  3. Jiangang Ma
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  4. Yanchun Zhang
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  5. Xin-Tian Chen
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Correspondence to Chun-Yang Ruan.

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Ruan, CY., Wang, Y., Ma, J. et al. Adversarial Heterogeneous Network Embedding with Metapath Attention Mechanism. J. Comput. Sci. Technol. 34, 1217–1229 (2019). https://doi.org/10.1007/s11390-019-1971-3

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  • DOI: https://doi.org/10.1007/s11390-019-1971-3

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