A Generative Model for the Layers of Terrorist Networks
Cohick, David S.
Castro, Victor G.
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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.
The article of record as published may be found at http://dx.doi.org/10.1145/3110025.3110153
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