Criminal Network Community Detection Using Graphical Analytic Methods: A Survey

Sangkaran, Theyvaa and Abdullah, Azween and JhanJhi, NZ. (2020) Criminal Network Community Detection Using Graphical Analytic Methods: A Survey. EAI Endorsed Transactions on Energy Web, 7 (27). ISSN 2032-944X

[thumbnail of eai.13-7-2018.162690.pdf]
eai.13-7-2018.162690.pdf - Published Version
Available under License Creative Commons Attribution No Derivatives.

Download (3MB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Community Detection, Criminal Network, Graph Analysis, Investigation, and Social Network Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 17 Sep 2020 10:50
Last Modified: 17 Sep 2020 10:50

Actions (login required)

View Item
View Item