基于图神经网络的动态网络异常检测算法

Jia Yan Guo, Rong Hua Li*, Yan Zhang, Guo Ren Wang

*此作品的通讯作者

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26 引用 (Scopus)
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摘要

Dynamic graph structured data is ubiquitous in real-life applications. Mining outliers on dynamic networks is an important problem, which is very useful for many practical applications. Most traditional network outlier detection algorithms focus mainly on the strutraulal anomaly, ignoring the nodes and edges' attributes, and the time-varying features as well. This study proposes a graph neural network based network anomaly detection algorithm which can capture the nodes and edges' attributes and time-varying features and fully uses these features to learn a representation vector for each node. Specifically, the proposed algorithm improves an unsupervised graph neural network framework called DGI. Based on DGI, a new danamic DGI algorithm is proposed, which is called Dynamic-DGI, for dynamic networks. Dynamic-DGI can simultaneously extracts the abnormal characteristics of the network itself and the abnormal characteristics of the network changes. The experimental results show that the proposed algorithm is better than the state-of-the-art anomaly detection algorithm SpotLight, and is significantly better than the traditional network representation learning algorithms. In addition to improving the accuracy, the proposed algorithmis also able to mine interesting anomalies in the network.

投稿的翻译标题Graph Neural Network Based Anomaly Detection in Dynamic Networks
源语言繁体中文
页(从-至)748-762
页数15
期刊Ruan Jian Xue Bao/Journal of Software
31
3
DOI
出版状态已出版 - 1 3月 2020

关键词

  • Anomaly detection in dynamic network
  • Deep learning on graphs
  • Graph neural network

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引用此

Guo, J. Y., Li, R. H., Zhang, Y., & Wang, G. R. (2020). 基于图神经网络的动态网络异常检测算法. Ruan Jian Xue Bao/Journal of Software, 31(3), 748-762. https://doi.org/10.13328/j.cnki.jos.005903