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Reviewed article

Inductive Discovery of Criminal Group Structure Using Spectral Embedding

How to cite:
Francesco Calderoni, David Skillicorn, Quan Zheng
"Inductive Discovery of Criminal Group Structure Using Spectral Embedding"
Information & Security: An International Journal,
31,
(2014):
49-66.
http://dx.doi.org/10.11610/isij.3102

Inductive Discovery of Criminal Group Structure Using Spectral Embedding

Source:

Information & Security: An International Journal,
Volume: 31,
p.49-66
(2014)

Abstract:

Social network analysis has often been applied to the content of communications within criminal groups to understand their internal structure and dynamics. While access to the content of communications may be restricted by constitutional and procedural constraints, this article applies advanced network analysis techniques based on spectral embedding to telephone communications data. Spectral embedding facilitates deeper analysis by embedding the graph representing a social network in a geometric space such that Euclidean distance reflects pairwise node dissimilarity. This enables drawing a network in ways that accurately reflect the structure of the underlying group, and computing properties directly from the embedding. We illustrate spectral approaches for two Ndrangheta drug-smuggling networks, and extend them to a) examine triad structure (through the identification of the Simmelian backbone), which elicits key members, and b) to display temporal properties, which illustrates changing group structure. The results show that the two groups, although having the same purpose and coming from the same criminal milieu, have substantially different internal structure which was not detectable using conventional social-network approaches. The techniques presented in this study may support law enforcement in the early stages of an investigation.

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18
Citations
Social Network Analysis and Mining
11
(2021):
85.
Doctor of Philosophy in Criminology
(2019):
Department of Computer Science Rensselaer
Doctor of philosophy
(2019):
IEEE International Conference on Intelligence and Security Informatics (ISI)
(2015):
School of Computing
Doctor of Philosophy
(2016):
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
(2018):
Information & Security: An International Journal
31
(2014):
05-06.