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用于半监督分类的二阶近似谱图卷积模型

公沛良 艾丽华

公沛良, 艾丽华. 用于半监督分类的二阶近似谱图卷积模型. 自动化学报, 2021, 47(5): 1067−1076 doi: 10.16383/j.aas.c200040
引用本文: 公沛良, 艾丽华. 用于半监督分类的二阶近似谱图卷积模型. 自动化学报, 2021, 47(5): 1067−1076 doi: 10.16383/j.aas.c200040
Gong Pei-Liang, Ai Li-Hua. Two-order approximate spectral convolutional model for semi-supervised classification. Acta Automatica Sinica, 2021, 47(5): 1067−1076 doi: 10.16383/j.aas.c200040
Citation: Gong Pei-Liang, Ai Li-Hua. Two-order approximate spectral convolutional model for semi-supervised classification. Acta Automatica Sinica, 2021, 47(5): 1067−1076 doi: 10.16383/j.aas.c200040

用于半监督分类的二阶近似谱图卷积模型

doi: 10.16383/j.aas.c200040
基金项目: 国家自然科学基金(61472029, 51827813, 61473031)资助
详细信息
    作者简介:

    公沛良:北京交通大学计算机与信息技术学院硕士研究生. 主要研究方向为图数据分析, 数据挖掘, 机器学习和认知计算. E-mail: plgong@bjtu.edu.cn

    艾丽华:博士, 北京交通大学计算机与信息技术学院副教授. 主要研究方向为大型图数据挖掘, 神经网络计算, 机器学习, 并行计算和分布式计算. 本文通信作者.E-mail: lhai@bjtu.edu.cn

Two-order Approximate Spectral Convolutional Model for Semi-Supervised Classification

Funds: Supported by National Natural Science Foundation of China (61472029, 51827813, 61473031)
More Information
    Author Bio:

    GONG Pei-Liang Master student at the School of Computer and Information Technology, Beijing Jiaotong University. His research interest covers graph data analysis, data mining, machine learning, and cognitive computing

    AI Li-Hua  Ph.D., associate professor at the School of Computer and Information Technology, Beijing Jiaotong University. Her research interest covers large-scale graph mining, neural network computing, machine learning, parallel computing, and distributed computing. Corresponding author of this paper

  • 摘要:

    近年来, 基于局部一阶近似的谱图卷积方法在半监督节点分类任务上取得了明显优势, 但是在每次更新节点特征表示时, 只利用了一阶邻居节点信息而忽视了非直接邻居节点信息. 为此, 本文结合切比雪夫截断展开式及标准化的拉普拉斯矩阵, 通过推导及简化二阶近似谱图卷积模块, 提出了一种融合丰富局部结构信息的改进图卷积模型, 进一步提高了节点分类性能. 大量的实验结果表明, 本文提出的方法在不同数据集上的表现均优于现有的流行方法, 验证了模型的有效性.

  • 图  1  本文构造的二阶邻域谱图卷积网络描述图

    Fig.  1  A schematic diagram of our two-order neighborhood spectral convolution network

    图  2  Cora上不同训练集大小(每个类的标记节点数)的准确率

    Fig.  2  Accuracy for different training set sizes (number of labeled nodes per class) on Cora

    图  4  PubMed上不同训练集大小(每个类的标记节点数)的准确率

    Fig.  4  Accuracy for different training set sizes (number of labeled nodes per class) on PubMed

    图  3  CiteSeer上不同训练集大小(每个类的标记节点数)的准确率

    Fig.  3  Accuracy for different training set sizes (number of labeled nodes per class) on CiteSeer

    图  5  目标节点t的一阶及两阶邻域组成的局部网络示意图

    Fig.  5  Schematic diagram of local network composed of the first-order and two-order neighborhoods of target node t

    图  6  在3个数据集上模型隐含层学习到的隐特征表示的t-SNE图

    Fig.  6  A t-SNE plot of the learned hidden feature representations of the model’s hidden layer on the three datasets

    表  1  4个数据集的基本统计信息

    Table  1  Basic statistics information for four datasets

    数据集节点特征类别
    CiteSeer 3327 473237036
    Cora 2708 542914337
    PubMed19717 44338 5003
    NELL657552661445414210
    下载: 导出CSV

    表  2  分类准确率结果汇总(%)

    Table  2  Summary of results in terms of classification accuracy (%)

    算法CiteSeerCoraPubMedNELL
    ManiReg60.159.570.721.8
    SemiEmb59.659.071.126.7
    LP45.368.063.026.5
    DeepWalk43.267.265.358.1
    ICA69.175.173.923.1
    Planetoid64.775.777.261.9
    SpectralCNN58.973.373.9
    Cheby-Net69.881.274.4
    Monet81.778.8
    GCN70.381.579.066.0
    本文算法71.882.679.867.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-19
  • 录用日期:  2020-04-16
  • 网络出版日期:  2021-05-21
  • 刊出日期:  2021-05-20

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