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融合知识的多视图属性网络异常检测模型

杜航原 曹振武 王文剑 白亮

杜航原, 曹振武, 王文剑, 白亮. 融合知识的多视图属性网络异常检测模型. 自动化学报, 2023, 49(8): 1732−1744 doi: 10.16383/j.aas.c220629
引用本文: 杜航原, 曹振武, 王文剑, 白亮. 融合知识的多视图属性网络异常检测模型. 自动化学报, 2023, 49(8): 1732−1744 doi: 10.16383/j.aas.c220629
Du Hang-Yuan, Cao Zhen-Wu, Wang Wen-Jian, Bai Liang. Multi-view outlier detection for attributed network based on knowledge fusion. Acta Automatica Sinica, 2023, 49(8): 1732−1744 doi: 10.16383/j.aas.c220629
Citation: Du Hang-Yuan, Cao Zhen-Wu, Wang Wen-Jian, Bai Liang. Multi-view outlier detection for attributed network based on knowledge fusion. Acta Automatica Sinica, 2023, 49(8): 1732−1744 doi: 10.16383/j.aas.c220629

融合知识的多视图属性网络异常检测模型

doi: 10.16383/j.aas.c220629
基金项目: 国家自然科学基金 (U21A20513, 62076154, 61902227, 62022052, 62276159), 山西省重点研发计划项目(202202020101003)资助
详细信息
    作者简介:

    杜航原:山西大学计算机与信息技术学院副教授. 主要研究方向为数据挖掘和机器学习. 本文通信作者. E-mail: duhangyuan@sxu.edu.cn

    曹振武:山西大学计算机与信息技术学院硕士研究生. 主要研究方向为数据挖掘和机器学习. E-mail: caozhenwu_sxu@126.com

    王文剑:山西大学计算机与信息技术学院教授. 主要研究方向为机器学习, 数据挖掘和人工智能. E-mail: wjwang@sxu.edu.cn

    白亮:山西大学智能信息处理研究所教授. 主要研究方向为机器学习, 数据挖掘和数据科学与大数据计算. E-mail: bailiang@sxu.edu.cn

Multi-view Outlier Detection for Attributed Network Based on Knowledge Fusion

Funds: Supported by National Natural Science Foundation of China (U21A20513, 62076154, 61902227, 62022052, 62276159) and the Key R&D Program of Shanxi Province (202202020101003)
More Information
    Author Bio:

    DU Hang-Yuan Associate professor at the School of Computer and Information Technology, Shanxi University. His research interest covers data mining and machine learning. Corresponding author of this paper

    CAO Zhen-Wu Master student at the School of Computer and Information Technology, Shanxi University. His research interest covers data mining and machine learning

    WANG Wen-Jian Professor at the School of Computer and Information Technology, Shanxi University. Her research interest covers machine learning, data mining and artificial intelligence

    BAI Liang Professor at the Institute of Intelligent Information Processing, Shanxi University. His research interest covers machine learning, data mining, data science and big data computing

  • 摘要: 属性网络异常检测在网络安全、电子商务和金融交易等领域中具有重要的理论与现实意义, 近年来受到了越来越多的关注. 大多数异常检测方法凭借网络有限的属性或结构信息进行决策生成, 往往难以对异常模式做出可靠的描述. 此外, 网络节点对应的实体往往关联着丰富的领域知识, 这些知识对于异常的识别具有重要的潜在价值. 针对上述情况, 提出一种融合知识的多视图网络异常检测模型, 在多视图学习模式下通过数据与知识的互补融合实现了对异常节点的有效识别. 首先, 使用TransR模型由领域知识图谱抽取知识向量表示, 并借助输入网络的拓扑关系构造其孪生网络. 接着, 在多视图学习框架下构建属性编码器和知识编码器, 分别将属性网络及其孪生网络嵌入到各自的表示空间, 并聚合为统一网络表示. 最后, 综合不同维度上的重构误差进行节点异常分数评价, 从而识别网络中的异常节点. 在真实网络数据集上的对比实验表明, 提出的模型能够实现对领域知识的有效融合, 并获得优于基线方法的异常检测性能.
  • 图  1  电商网络中的属性信息与知识

    Fig.  1  Attribute information and knowledge in the e-commerce network

    图  2  MOD-KF模型总体框架

    Fig.  2  The overall framework of the MOD-KF model

    图  3  各方法在不同数据集上的ROC曲线

    Fig.  3  ROC curves of each method on different datasets

    图  4  消融实验结果

    Fig.  4  Detection results of ablation experiment

    图  5  不同$ \lambda$值对检测结果的影响

    Fig.  5  Impact of different $ \lambda $ values on detection result

    图  6  嵌入维度对检测结果的影响

    Fig.  6  Impact of different embedding dimensions on detection result

    图  7  不同注意力头数对检测结果的影响

    Fig.  7  Impact of different attention heads on detection result

    表  1  实验数据集统计信息

    Table  1  Statistics of datasets in experiment

    数据集网络特性领域知识
    节点属性异常率实体关系三元组
    AmazonBooks24 91528128 7420.0247124 32093541 853
    MoviesLens2 1822031 5730.052250 87552181 639
    Last.FM23 5668187 4720.025847 98612325 147
    下载: 导出CSV

    表  2  各方法在不同数据集上的AUC值

    Table  2  AUC values of each method on different datasets

    方法AmazonBooksMovieLensLast.FM
    Radar0.72050.75860.6891
    GAAN0.8436 0.82030.7504
    Dominant0.7585 0.82460.7331
    SpecAE0.68240.73480.6706
    ALARM0.76430.81650.7729
    MOD-KF_Add0.82300.88520.8106
    MOD-KF_Concat0.83640.87430.8213
    下载: 导出CSV

    表  3  不同算法的Precision@K结果

    Table  3  Results of different algorithms in terms of Precision@K

    数据集K异常检测方法
    RadarGAANDominantSpecAEALARMMOD-KF_AddMOD-KF_Concat
    AmazonBooks500.82360.90080.84120.77840.84310.92480.9183
    1000.83930.90780.79270.80690.81130.91940.9146
    2000.75430.88510.73290.72080.78400.87420.8616
    5000.73400.86330.76240.69050.75170.85260.8519
    MovieLens50.80400.79600.98400.79200.96800.98000.9720
    100.84200.85400.89400.81800.89600.95400.9440
    500.71940.84050.83680.81770.85260.93570.9022
    1000.72710.81110.82240.76700.83650.89780.9095
    Last.FM500.81740.75790.81480.73920.83840.90020.8886
    1000.78550.74130.79690.71320.80850.88260.8871
    2000.72470.75540.73310.68070.79480.90530.8966
    5000.67160.75250.71430.63650.77430.86230.8704
    下载: 导出CSV

    表  4  不同算法的Recall@K结果

    Table  4  Results of different algorithms in terms of Recall@K

    数据集K异常检测方法
    RadarGAANDominantSpecAEALARMMOD-KF_AddMOD-KF_Concat
    AmazonBooks500.06310.07280.06620.06180.06600.07460.0743
    1000.13190.14710.13530.11850.13040.14860.1506
    2000.24250.28390.24950.23110.23510.27920.2805
    5000.58800.68100.60630.52110.59030.67760.6846
    MovieLens50.03220.03560.03530.02960.04230.04200.0416
    100.06840.07520.07810.06540.07330.08130.0852
    500.32380.32280.36110.31410.35990.40400.3961
    1000.63590.70410.70980.58110.71180.78620.7770
    Last.FM500.06590.06090.06440.06100.06560.07120.0724
    1000.10740.10860.11640.10340.11210.12210.1257
    2000.22910.23800.24750.22310.24200.27880.2771
    5000.54670.59900.59570.51310.63400.69310.7022
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-08
  • 录用日期:  2023-01-18
  • 网络出版日期:  2023-06-01
  • 刊出日期:  2023-08-21

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