Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2068-2075

Abnormal detection method based on spatial scale coarse-grained

Fu Kun1,2
Liu Qi1,2
Zhuo Jiaming1,2
Li Jianing1,2
Guo Yunpeng1,2
1. College of Artificial Intelligence & Data Science, Hebei University of Technology, Tianjin 300401, China
2. Key Laboratory of Big Data Computing, Tianjin 300401, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0649

Abstract

At present, most link-prediction based models on anomaly detection in social networks lack the ability to consider the influence of abnormal nodes evolution. With the limitation of network scale and complexity, the detection efficiency of traditional models is generally low. To address these issues, this paper proposed an anomaly detection method based on the spatial scale coarse granulation and weighting mechanism on abnormal nodes. Firstly, the method introduced a cohesive community discovery algorithm, Louvain algorithm, to coarsely granulate process to streamline network. Subsequently, it identified abnormal nodes with different evolution behaviors in the processed network following the quantification of abnormal evolution process. Finally, it applied the link prediction method combined with a weighting mechanism of abnormal nodes for final abnormal detection. Compared with different LinkEvent-based strategy adjustment algorithms and NESO_ED method on three real social network data sets VAST, Email-EU(dept1 and dept2) and Enron, the proposed method outperforms other state-of-the-art methods, can take into account both stability and sensitivity on anomaly detection tasks and more reasonably describe the network evolution process.

Foundation Support

国家自然科学基金资助项目(62072154)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0649
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2068-2075
Serial Number: 1001-3695(2022)07-023-2068-08

Publish History

[2022-02-16] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

富坤, 刘琪, 禚佳明, 等. 基于空间尺度粗粒化的异常检测方法 [J]. 计算机应用研究, 2022, 39 (7): 2068-2075. (Fu Kun, Liu Qi, Zhuo Jiaming, et al. Abnormal detection method based on spatial scale coarse-grained [J]. Application Research of Computers, 2022, 39 (7): 2068-2075. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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