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    Self-supervised Smoothing Graph Neural Networks

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    Type
    Preprint
    Authors
    Yu, Lu
    Pei, Shichao
    Zhang, Chuxu
    Ding, Lizhong
    Zhou, Jun
    Li, Longfei
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science
    Date
    2020-09-02
    Permanent link to this record
    http://hdl.handle.net/10754/666197
    
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    Abstract
    This paper studies learning node representations with GNNs for unsupervised scenarios. We make a theoretical understanding and empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the graph locality. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed KS2L - a novel graph \underline{K}nowledge distillation regularized \underline{S}elf-\underline{S}upervised \underline{L}earning framework, with two complementary regularization modules, for intra-and cross-model graph knowledge distillation. We demonstrate the competitive performance of KS2L on a variety of benchmarks. Even with a single GCN layer, KS2L has consistently competitive or even better performance on various benchmark datasets.
    Publisher
    arXiv
    arXiv
    2009.00934
    Additional Links
    https://arxiv.org/pdf/2009.00934
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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