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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1721-1727

Anomaly detection method based on graph modularity clustering

Fu Kun1,2
Liu Yinghua1,2
Hao Yuhan1,2
Sun Minglei1,2
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.1001-3695.2022.10.0513

Abstract

As the growth of social network scale, so do challenges to the existing anomaly detection algorithms. Therefore, this paper proposed an anomaly detection method based on graph modularity clustering(GMC_AD), which could be applied to solve the problem of low detection efficiency caused by network size and complexity. Based on analyzing the network topology structure, the GMC_AD method improved the efficiency of events detection by weighting mechanism on abnormal nodes and modularity clustering algorithm. The GMC_AD processes could be described as follow: a) Since designing a quantization strategy for node evolution in the network, GMC_AD get the set of abnormal nodes by recognizing nodes with abnormal evolutionary behaviors. b) The method used a modularity clustering algorithm to reduce the network size. c) During the calculation of network fluctuation value, it introduced a weighting mechanism for taking the influence of abnormal nodes into consideration, after that, the GMC_AD method detected the abnormality by the changes of network fluctuation value. On real social network datasets VAST, EU_E-mail and ENRON, the GMC_AD method accurately detected the abnormal periods. The event detection sensibility of GMC_AD method was increased by 50%~82% meanwhile the run-time efficiency increased by 30%~70%. The GMC_AD method enhances not only the accuracy and sensitivity but also the efficiency of anomaly detections.

Foundation Support

国家自然科学基金资助项目(61806072)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0513
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1721-1727
Serial Number: 1001-3695(2023)06-019-1721-07

Publish History

[2023-01-05] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

富坤, 刘赢华, 郝玉涵, 等. 基于图模块度聚类的异常检测算法 [J]. 计算机应用研究, 2023, 40 (6): 1721-1727. (Fu Kun, Liu Yinghua, Hao Yuhan, et al. Anomaly detection method based on graph modularity clustering [J]. Application Research of Computers, 2023, 40 (6): 1721-1727. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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