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Title

Community Detection Based on Isomorphic Subgraph Analytics in Criminal Network

Author

Theyvaa Sangkaran, Azween Abdullah, NZ. Jhanjhi

Citation

Vol. 20  No. 5  pp. 94-102

Abstract

Community detection using graph theory allows us to detect a community within an organized criminal syndicate that has network orientated structures. Classical community detection methods will have problems to detect communities with different network orientated structures even though they have similar nodes. Studying the inter-connections between the nodes by employing isomorphic subgraph analytics allows the researchers and law enforcement agencies to understand and to determine the key participants and the criminals’ modus operandi of illicit operations. One of the domains which we have selected to work on is criminal network analysis as there is a lack of new perspective in the Criminal Network Analysis (CNA), which is urgently required as the modus operandi behind crimes are considerably complex now. We studied community detection in criminal networks using graph theory and formally introduced an algorithm that opened a new perspective of community detection compared to the traditional methods used to model the relations between objects using the isomorphic graph-based analytics. Community structure is an important property of complex networks, which is generally described as densely connected nodes and similar patterns of links. Our method differed from the traditional methods because our method allowed the law enforcement agencies to compare the detected communities, and this would allow a different point of view of the criminal network. This research allowed and assisted enforcement agencies and researchers to detect the same community from different patterns and structures by employing isomorphism. This would allow the detection of the communities that may not have been found using the traditional methods.

Keywords

Cybersecurity, Graph Theory, Criminal Network, Ad-hoc networks, Social Network

URL

http://paper.ijcsns.org/07_book/202005/20200512.pdf