Computer Science ›› 2021, Vol. 48 ›› Issue (6): 332-337.doi: 10.11896/jsjkx.200700151

• Information Security • Previous Articles     Next Articles

Malicious User Detection Method for Social Network Based on Active Learning

ZHANG Ren-zhi, ZHU Yan   

  1. School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2020-07-24 Revised:2020-09-16 Online:2021-06-15 Published:2021-06-03
  • About author:ZHANG Ren-zhi,born in 1996,postgraduate.His main research interests include Web spam detection and graph neural network.(
    ZHU Yan,born in 1965,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include data mining,Web anomaly detection,big data mana-gement and intelligent analysis.
  • Supported by:
    Sichuan Science and Technology Project(2019YFSY0032).

Abstract: As a classification task,malicious user detection needs to label training samples.However,the scale of social networks is usually large,and it costs a lot to label all samples.In order to find out the more worthy samples in the case of limited labeled budget,and make full use of unlabeled samples to improve the detection performance of malicious users,a detection method based on graph neural network and active learning is proposed.The method is divided into two parts:detection module and active lear-ning module.Inspired by Transformer,the detection module improves the graph neural network GraphSAGE,flattens the aggregation process of each order neighbors of its nodes,so that higher-order neighbors can directly aggregate to the central node and reduce the information loss of high-order neighbors.Then,through ensemble learning,the extracted representations are used from different perspectives to complete the detection task.The active learning module measures the value of unlabeled samplesaccor-ding to the results of ensemble classification,and alternately uses detection module and active learning module in the sample labeling stage to guide the process of labeling sample,which is more conducive to the model classification.In the experimental stage,AUROC and AUPR are used as evaluation indexes to verify the effectiveness of the improved detection module on a real large-scale social network data set,and the reasons for the improvement are analyzed.Then,compared with the existing two similar active learning methods,the experimental results show that the proposed method has better classification performance in the case of labeling the same number of training samples.

Key words: Active learning, Graph neural network, Imbalanced data, Malicious user detection, Social network

CLC Number: 

  • TP183
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