Detecting significant changes in dark networks
Abstract
To date, most social network analyses (SNAs) of terrorist groups have used
network data that provide snap-shots of the groups at a single point in time.
Seldom have they used network data that take into account how the groups have
changed over time. In this article, a unique longitudinal network data set, the
Noordin Top terrorist network from 2001 to 2010, is examined in order to
explore whether a recently developed method – social network change detection
(SNCD) – can help analysts monitor a dark network’s topography (e.g.
centralization, density, degree of fragmentation) in order to detect significant
changes in its structure and identify possible causes. The application of change
detection to this historical data set illustrates the method’s potential usefulness,
including its ability to detect significant changes in the network in response to a
series of exogenous factors, such as the acquisition of bombing materials, the
capture of key leaders and groups, and the death of Noordin himself. The
method’s inability to detect other significant events, however, highlights
important limitations when working with it. While SNCD should not be the only
method analysts have at their disposal, the results detailed in this article suggest
that it should be included in their toolkit.
Description
The article of record as published may be found at http://dx.doi.org/10.1080/19434472.2012.725225
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.Collections
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