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Dynamic social network analysis of a dark network: Identifying significant facilitators


Kaza, Siddharth; Hu, Daning; Chen, Hsinchun (2007). Dynamic social network analysis of a dark network: Identifying significant facilitators. In: the 5th IEEE Conference on Intelligence and Security Informatics, New Brunswick, New Jersey, 23 May 2007 - 24 May 2007, 40-46.

Abstract

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Event End Date:24 May 2007
Deposited On:20 Jul 2012 08:43
Last Modified:05 Apr 2016 15:44
Publisher:IEEE
Publisher DOI:https://doi.org/10.1109/ISI.2007.379531
Other Identification Number:merlin-id:6808
Permanent URL: https://doi.org/10.5167/uzh-61045

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