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    Multiple clusterings of heterogeneous information networks

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    NetMCs.pdf
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    Description:
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    Type
    Article
    Authors
    Wei, Shaowei
    Yu, Guoxian cc
    Wang, Jun
    Domeniconi, Carlotta
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2021-06-02
    Online Publication Date
    2021-06-02
    Print Publication Date
    2021-06
    Embargo End Date
    2022-06-02
    Submitted Date
    2020-11-21
    Permanent link to this record
    http://hdl.handle.net/10754/669566
    
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    Abstract
    Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this problem, approaches that exploit meaningful alternative clusterings in data have been developed in recent years. Existing algorithms, including single view/multi-view multiple clustering methods, are designed for applications with i.i.d. data samples, and cannot handle the data samples with dependency presented in networks, especially in heterogeneous information networks (HIN). In this paper, we propose a framework (NetMCs) that can explore multiple clusterings in HIN. Specifically, NetMCs adopts a set of meta-path schemes with different semantics on HIN, and considers each meta-path scheme as a base clustering aspect. Guided by the meta-path schemes, NetMCs then introduces a variation of the skip-gram framework that can jointly optimize multiple clustering aspects, and simultaneously obtain the respective embedding representations and individual clusterings therein. To reduce redundancy between alternative clusterings, NetMCs utilizes an explicit regularization term to control the embedding diversity of the same nodes among different clustering aspects. Experiments on benchmark HIN datasets confirm the performance of NetMCs in generating multiple clusterings with high quality and diversity.
    Citation
    Wei, S., Yu, G., Wang, J., Domeniconi, C., & Zhang, X. (2021). Multiple clusterings of heterogeneous information networks. Machine Learning. doi:10.1007/s10994-021-06000-y
    Sponsors
    This work is partially supported by NSFC (Nos. 62031003, 62072380 and 61872300).
    Publisher
    Springer Science and Business Media LLC
    Journal
    Machine Learning
    DOI
    10.1007/s10994-021-06000-y
    Additional Links
    https://link.springer.com/10.1007/s10994-021-06000-y
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10994-021-06000-y
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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