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    Co-Embedding Attributed Networks

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    attribute.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Meng, Zaiqiao
    Liang, Shangsong
    Bao, Hongyan
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    KAUST Grant Number
    FCC/1/1976-19-01
    Date
    2019-03-11
    Online Publication Date
    2019-03-11
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/631764
    
    Metadata
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    Abstract
    Existing embedding methods for attributed networks aim at learning low-dimensional vector representations for nodes only but not for both nodes and attributes, resulting in the fact that they cannot capture the affinities between nodes and attributes. However, capturing such affinities is of great importance to the success of many real-world attributed network applications, such as attribute inference and user profiling. Accordingly, in this paper, we introduce a Co-embedding model for Attributed Networks (CAN), which learns low-dimensional representations of both attributes and nodes in the same semantic space such that the affinities between them can be effectively captured and measured. To obtain high-quality embeddings, we propose a variational auto-encoder that embeds each node and attribute with means and variances of Gaussian distributions. Experimental results on real-world networks demonstrate that our model yields excellent performance in a number of applications compared with state-of-the-art techniques.
    Citation
    Meng Z, Liang S, Bao H, Zhang X (2019) Co-Embedding Attributed Networks. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM ’19. Available: http://dx.doi.org/10.1145/3289600.3291015.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-19-01.
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19
    Conference/Event name
    12th ACM International Conference on Web Search and Data Mining, WSDM 2019
    DOI
    10.1145/3289600.3291015
    Additional Links
    https://dl.acm.org/citation.cfm?doid=3289600.3291015
    ae974a485f413a2113503eed53cd6c53
    10.1145/3289600.3291015
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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