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.

[2023-01-05] Accepted Paper /doc/2022.10.0513/v1

[2023-06-05] Printed Article /doc/2022.10.0513/v2