Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these methods are viable enough to be used as supporting evidence by security forces in their fight against criminal organizations operating on social networks.
ÇİNAR, MUHAMMET SERKAN; GENÇ, BURKAY; and SEVER, HAYRİ
"Identifying criminal organizations from their social network structures,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
1, Article 31.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss1/31