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Adversarial network embedding on heterogeneous information networks

Journal of physics. Conference series, 2020-12, Vol.1693 (1), p.12018 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1693/1/012018

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  • Title:
    Adversarial network embedding on heterogeneous information networks
  • Author: Lan, Ting ; Wu, Changxuan ; Yu, Chunyan ; Wang, Xiu
  • Subjects: Clustering ; Embedding ; Networks ; Perturbation ; Smoothness
  • Is Part Of: Journal of physics. Conference series, 2020-12, Vol.1693 (1), p.12018
  • Description: Network embedding has been proven to be helpful for solving real-world problems. Moreover, real-world networks are often heterogeneous information networks(HINs). In this paper, we propose a new adversarial framework for heterogeneous network embedding, namely AGNE-HIN. AGNE-HIN can learn latent code distribution in the network through a generative adversarial way. What's more, to reduce the global smoothness of the embedded vector caused by GAN, we apply perturbation to the input to form adversarial data. Experimental results verify our design and demonstrate the effectiveness of the proposed method in node clustering, link prediction and similarity ranking tasks.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1693/1/012018
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    ProQuest Central
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