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    Collaborative graph walk for semi-supervised multi-label node classification

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    Type
    Conference Paper
    Authors
    Akujuobi, Uchenna Thankgod cc
    Yufei, Han
    Zhang, Qiannan
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    King Abdullah University of Science and Technology (KAUST), Saudi Arabia
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-01-31
    Permanent link to this record
    http://hdl.handle.net/10754/660643
    
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    Abstract
    In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named Multi-Label-Graph-Walk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multi-label node classification task as simultaneous graph walks conducted by multiple label-specific agents. Furthermore, policies of the label-wise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple label-specific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multi-label classification performance than the state-of-the-art approaches and conduct more efficient graph exploration.
    Citation
    Akujuobi, U., Yufei, H., Zhang, Q., & Zhang, X. (2019). Collaborative Graph Walk for Semi-Supervised Multi-label Node Classification. 2019 IEEE International Conference on Data Mining (ICDM). doi:10.1109/icdm.2019.00010
    Sponsors
    This work is supported by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia
    Publisher
    IEEE
    Conference/Event name
    19th IEEE International Conference on Data Mining, ICDM 2019
    DOI
    10.1109/ICDM.2019.00010
    arXiv
    1910.09706
    Additional Links
    https://ieeexplore.ieee.org/document/8970680/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDM.2019.00010
    Scopus Count
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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