基于结构深度网络嵌入模型的节点标签分类算法

doi:10.11896/jsjkx.201000177

计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 105-112.doi: 10.11896/jsjkx.201000177

• 数据库&大数据&数据科学 • 上一篇下一篇

基于结构深度网络嵌入模型的节点标签分类算法

陈世聪¹, 袁得嵛^1,2, 黄淑华^1,2, 杨明^1,2

1 中国人民公安大学信息网络安全学院北京100038
2 安全防范与风险评估公安部重点实验室北京100038

收稿日期:2020-10-29 修回日期:2021-05-23 出版日期:2022-03-15 发布日期:2022-03-15
通讯作者: 袁得嵛(yuandeyu@ppsuc.edu.cn)
作者简介:(chenshicong24@163.com)
基金资助:
国家社会科学基金重点项目(20AZD114);中国人民公安大学基本科研业务费项目(2021JKF215);中国人民公安大学公共安全行为科学实验室开放课题(2020SYS03);警务物联网应用技术公安部重点实验室开放课题

Node Label Classification Algorithm Based on Structural Depth Network Embedding Model

CHEN Shi-cong¹, YUAN De-yu^1,2, HUANG Shu-hua^1,2, YANG Ming^1,2

1 School of Information and Cyber Security,People’s Public Security University of China,Beijing 100038,China
2 Key Laboratory of Safety Precautions and Risk Assessment,Ministry of Public Security,Beijing 100038,China

Received:2020-10-29 Revised:2021-05-23 Online:2022-03-15 Published:2022-03-15
About author:CHEN Shi-cong,born in 1997,master.His main research interests include cyberspace security and law enforcement technology.
YUAN De-yu,born in 1986,Ph.D,lecturer,Ph.D supervisor.His main research interests include cyber security and complex networks.
Supported by:
National Social Science Foundation of China(20AZD114),Fundamental Research Funds for the Central Universities(2021JKF215),Open Research Fund of the Public Security Behavioral Science Laboratory,People’s Public Security University of China(2020SYS03) and Open Research Fund of Key Laboratory of the Police Internet of Things Application Technology.

摘要/Abstract

摘要： 在海量数据呈现爆炸增长态势的互联网时代,传统算法已无法满足处理大规模、多类型数据的需求。近年来最新的图嵌入算法通过学习图网络特征,在链路预测、网络重构和节点分类实践中普遍取得了极佳的效果。文中基于传统自动编码器模型,创新地提出了一种融合Sdne算法与链路预测相似度矩阵的新算法,通过在反向传播过程中引入高阶损失函数,依据自编码器的新特征调整性能,改进传统算法中以单一方式判定节点相似度这一方法存在的弊端,并建立简易模型分析证明优化的合理性。对比最新研究中效果最好的Sdne算法,该算法在Micro-F1和Macro-F1两种评价指标上的提升效果均接近1%,可视化分类效果表现良好。与此同时,研究发现高阶损失函数超参的最优值大致处于1~10范围内,数值的变化依旧能够基本稳定维持整体网络的鲁棒性。

关键词: 复杂网络, 节点分类, 深度学习, 网络嵌入, 自动编码器

Abstract: In the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and node classification by learning graph network characteristics.Based on the traditional automatic encoder model,a new algorithm combining Sdne algorithm and link prediction similarity matrix is proposed.By introducing a high-order loss function in the process of back-propagation,the performance is adjusted according to the new characteristics of the auto-encoder.The disadvantages of traditional algorithm in determining node similarity in a single way are improved.A simple model is established to analyze and prove the rationality of the optimization.Compared with the most effective Sdne algorithm in the latest research,the improvement effect of this algorithm on Micro-F1 and Macro-F1 two evaluation indicators is close to 1%,and the visual classification effect is good.At the same time,it is found that the optimal value of the hyperparameter of the higher-order loss function is approximately in the range of 1~10,and the change of the numerical value can basically maintain the robustness of the whole network.

Key words: Auto-encoder, Complex network, Deep learning, Network embedding, Node classification

中图分类号:

TP311

陈世聪, 袁得嵛, 黄淑华, 杨明. 基于结构深度网络嵌入模型的节点标签分类算法[J]. 计算机科学, 2022, 49(3): 105-112. https://doi.org/10.11896/jsjkx.201000177

CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming. Node Label Classification Algorithm Based on Structural Depth Network Embedding Model[J]. Computer Science, 2022, 49(3): 105-112. https://doi.org/10.11896/jsjkx.201000177

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基于结构深度网络嵌入模型的节点标签分类算法

Node Label Classification Algorithm Based on Structural Depth Network Embedding Model

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