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    Multi-modal Network Representation Learning

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    Type
    Conference Paper
    Authors
    Zhang, Chuxu
    Jiang, Meng
    Zhang, Xiangliang cc
    Ye, Yanfang
    Chawla, Nitesh V.
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-08-20
    Online Publication Date
    2020-08-20
    Print Publication Date
    2020-08-23
    Permanent link to this record
    http://hdl.handle.net/10754/665223
    
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    Abstract
    In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.
    Citation
    Zhang, C., Jiang, M., Zhang, X., Ye, Y., & Chawla, N. V. (2020). Multi-modal Network Representation Learning. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3394486.3406475
    Publisher
    Association for Computing Machinery (ACM)
    Conference/Event name
    26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
    ISBN
    9781450379984
    DOI
    10.1145/3394486.3406475
    Additional Links
    https://dl.acm.org/doi/10.1145/3394486.3406475
    ae974a485f413a2113503eed53cd6c53
    10.1145/3394486.3406475
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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