ew 20(26): e5

Research Article

Criminal Network Community Detection Using Graphical Analytic Methods: A Survey

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  • @ARTICLE{10.4108/eai.13-7-2018.162690,
        author={Theyvaa Sangkaran and Azween Abdullah and NZ. JhanJhi},
        title={Criminal Network Community Detection Using Graphical Analytic Methods: A Survey},
        journal={EAI Endorsed Transactions on Energy Web},
        keywords={Community Detection, Criminal Network, Graph Analysis, Investigation, and Social Network Analysis},
  • Theyvaa Sangkaran
    Azween Abdullah
    NZ. JhanJhi
    Year: 2020
    Criminal Network Community Detection Using Graphical Analytic Methods: A Survey
    DOI: 10.4108/eai.13-7-2018.162690
Theyvaa Sangkaran1,*, Azween Abdullah1, NZ. JhanJhi1
  • 1: School of Computing and IT (SoCIT), Taylor’s University, Malaysia
*Contact email: theyvaasangkarankrishnan@sd.taylors.edu.my


Criminal networks analysis has attracted several numbers of researchers as network analysis gained its popularity among professionals and researchers. In this study, we have presented a comprehensive review of community detection methods based on graph analysis. The concept of community was vividly discussed as well as the algorithms for detecting communities within a network. Broad categorization of community detection algorithms was also discussed as well as a thorough review of detection algorithms which has been developed, implemented and evaluated by several authors in social network analysis. Most importantly, a strict review of researches based on the detection of a community in a criminal network was carried out revealing the strength and limitations of criminal network community detection methods. Thus, it becomes obvious through this study that more research activities is necessary and expected in order to further grow this research area.