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    Accurately Estimating User Cardinalities and Detecting Super Spreaders over Time

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    Name:
    [submit]cardinality_TKDE (1).pdf
    Size:
    5.145Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Jia, Peng
    Wang, Pinghui
    Zhang, Yuchao
    Zhang, Xiangliang cc
    Tao, Jing
    Ding, Jianwei
    Guan, Xiaohong
    Towsley, Don
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/661680
    
    Metadata
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    Abstract
    Online monitoring user cardinalities in graph streams is fundamental for many applications such as anomaly detection. These graph streams may contain edge duplicates and have a large number of user-item pairs, which makes it infeasible to exactly compute user cardinalities due to limited computational and memory resources. Existing methods are designed to approximately estimate user cardinalities, but their accuracy highly depends on complex parameters and they cannot provide anytime-available estimation. To address these problems, we develop novel bit/register sharing algorithms, which use a bit/register array to build a compact sketch of all users' connected items. Our algorithms exploit the dynamic properties of the bit/register arrays (e.g., the fraction of zero bits in the bit array) to significantly improve the estimation accuracy, and have low time complexity O(1) to update the estimations for a new user-item pair. In addition, our algorithms are simple and easy to use, without requirements to tune any parameter. Furthermore, we extend our methods to detect super spreaders with large cardinalities in real-time. We evaluate the performance of our methods on real-world datasets. The experimental results demonstrate that our methods are several times more accurate and faster than state-of-the-art methods using the same amount of memory.
    Citation
    Jia, P., Wang, P., Zhang, Y., Zhang, X., Tao, J., Ding, J., … Towsley, D. (2020). Accurately Estimating User Cardinalities and Detecting Super Spreaders over Time. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2020.2975625
    Sponsors
    The research presented in this paper is supported in part by National Key R&D Program of China (2018YFC0830500), National Natural Science Foundation of China (U1736205, 61603290), Shenzhen Basic Research Grant (JCYJ20170816100819428), Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6034).
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2020.2975625
    Additional Links
    https://ieeexplore.ieee.org/document/9007395/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9007395
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2020.2975625
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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