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.
