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Monitoring and Disrupting Dark Networks: A Bias Toward the Center and What It Costs Us

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Eradicating Terrorism from the Middle East

Part of the book series: Public Administration, Governance and Globalization ((PAGG,volume 17))

Abstract

The goal of this chapter is to explore this analytic bias—how it is manifested, why it appears so extensive, and what unwitting limitations it imposes on our strategic options to counterterrorism. We use data from a study of the Syrian opposition network that was conducted in the CORE Lab at the Naval Postgraduate School in Monterey California (Lucente and Wilson, Crossing the red line: social media and social network analysis for unconventional campaign planning, 2013). The original study sought to provide a window into the armed opposition units against the regime of Syrian President Bashar Assad. This chapter proceeds as follows: We begin by reviewing the various strategies that can be used for disrupting dark networks. These can be broken down into two broad categories—kinetic and nonkinetic. The former uses coercive means for disruption while the latter seeks to undermine dark networks using with subtler applications of power. Drawing on a previous analysis, we illustrate how some of these strategies can be implemented, while at the same time highlighting our own bias in that study toward central actors. We then turn to an analysis of the Syrian opposition network, highlighting how a central focus can blind analysts to other important aspects of a network; in this case, elements that ultimately aligned themselves with the Islamic State of Iraq and Syria (ISIS). We conclude with some implications for the future use of SNA to monitor and disrupt dark networks.

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Notes

  1. 1.

    Dark networks are defined here as illegal and covert networks (Raab and Milward 2003).

  2. 2.

    Our original article included only four nonkinetic strategies. A fifth was added to a later publication (Everton 2012).

  3. 3.

    These multirelational networks are referred to as multiplex (multiple types of relational networks) that are combined and “stacked” together.

  4. 4.

    Unless otherwise noted, we created the network graphs presented in this paper with the social analysis tool, Pajek (Batagelj and Mrvar 2015).

  5. 5.

    The original researchers treated the multimodal data as what social network refer to as a one-mode network, which was technically incorrect since individuals are generally considered a different type of actor than political groups and military units. Ideally, they would have coded all of the data at the individual level using the leaders of the military units and political groups rather than the units and groups themselves. That granular level of data was simply unavailable, however, so the military units and political groups essentially functioned as “stand-ins” for the leaders of those units and groups. Thus, it is legitimate to treat and analyze the network as a one-mode network. The data have also been refined and cleaned since the original analysis, so the number of actors in the network is somewhat different. The network’s structure remains essentially the same, however.

  6. 6.

    This is not the same network graph presented by Lucente and Wilson (2013: 25, Fig. 2), which they generated using the graph drawing program , Gephi (Bastian et al. 2009). It shows the network as consisting of two main clusters separated by a central cluster of a small group of actors whereas here the central cluster is broken down into three separate but central clusters.

  7. 7.

    The standard centralization algorithm calculates the variation between the centrality scores of all actors in the network with the highest centrality score in the network. See Everton (2012:152). Centralization indices were calculated with the social network analysis program, UCINET (Borgatti et al. 2002).

  8. 8.

    There are numerous social network analysis clustering algorithms that assign actors to distinct subnetworks based on the network’s pattern of ties. In general, these algorithms assume that ties within a subnetwork are denser than across subnetworks. The Louvain method is a widely accepted clustering algorithm that has been implemented in numerous social network analysis packages. In Fig. 8 node color indicates the subnetworks to which the various actors have been assigned by the Louvain algorithm.

  9. 9.

    This was determined by the names of the individuals, political organizations, and military units included in this subnetwork.

  10. 10.

    For example, a recent version of UCINET (Borgatti et al. 2002) includes at least 23 different types of centrality measures, which is far more than the number of cohesion measures it estimates (11) and clustering algorithms it implements (12).

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Authors and Affiliations

  1. Department of Defense Analysis, Naval Postgraduate School, Monterey, CA, 93943, USA

    Nancy Roberts & Sean Everton

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  1. Nancy Roberts
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  2. Sean Everton
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Correspondence to Nancy Roberts .

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  1. Marywood University, Scranton, Pennsylvania, USA

    Alexander R. Dawoody

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Roberts, N., Everton, S. (2016). Monitoring and Disrupting Dark Networks: A Bias Toward the Center and What It Costs Us. In: Dawoody, A. (eds) Eradicating Terrorism from the Middle East. Public Administration, Governance and Globalization, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-31018-3_2

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