Publication:
A Generative Model for the Layers of Terrorist Networks

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Authors
Adeniji, Oludare
Cohick, David S.
Gera, Ralucca
Castro, Victor G.
Saxena, Akrati
Subjects
Advisors
Date of Issue
2017-07-31
Date
Publisher
IEEE
Language
Abstract
Data about terrorist networks is sparse and not consistently tagged as desired for research. Moreover, such data collections are hard to come across, which makes it challenging to propose solutions for the dynamic phenomenon driving these networks. This creates the need for generative network models based on the existing data. Dark networks show different characteristics than the other scale-free real world networks, in order to maintain the covert nature while remaining functional. In this work, we present the analysis of the layers of three terrorist multilayered networks. Based on our analysis, we categorize these layers into two types: evolving and mature. We propose generative models to create synthetic dark layers of both types. The proposed models are validated using the available datasets and results show that they can be used to generate synthetic layers having properties similar to the original networks' layers.
Type
Conference Paper
Description
The article of record as published may be found at http://dx.doi.org/10.1145/3110025.3110153
Series/Report No
Department
Operations Research (OR)
Applied Mathematics
Computer Science (CS)
Other Units
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Department of Defense (DoD)
Funder
Format
8 p.
Citation
Adeniji, Oludare, David S. Cohick, Ralucca Gera, Victor G. Castro, and Akrati Saxena. "A Generative Model for the Layers of Terrorist Networks." In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 690-697. ACM, 2017.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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