Researchers using social network analysis have documented the structure of criminal organizations and groups, and have used existing methods and metrics to identify key actors in dark networks (e.g., degree and betweenness centrality). SNA measures focus on the connectivity or relationships between actors. However, actors in dark networks may be key for reasons unrelated to their connectivity. For example, they may play important roles such as obtaining critical resources. The removal of key actors is one strategy that may be used to disrupt and dismantle dark networks, and computer simulations have been used to evaluate the impact of arrests and other law enforcement interventions that seek to mitigate the efficacy of criminal organizations. This chapter assesses the value of such computer simulations and concludes that they offer valuable, if imperfect, insights into the structure and function of illicit networks. The Role of Research and the Science of Dark Network Disruption While law enforcement agencies utilize SNA methodologies to identify potential targets for surveillance and arrest, researchers across diverse disciplines including the social sciences, mathematics, and computer science study dark networks to develop understandings of their social structure and organization. Researchers have examined many different types of dark networks, including groups involved in price fixing in corporations (Baker & Faulkner, 1993), drug trafficking groups (Morselli & Petit, 2007; Bright, Hughes, & Chalmers, 2012), and terrorist groups (e.g., Krebs, 2002a, 2002b; Rogriguez, 2005; Koschade, 2006; Perliger & Pedahzur, 2011; Harris-Hogan, 2012). Much of this work has focused on descriptions of the structure of dark networks and the identification of key actors. The results of such research have implications for identifying areas of strength and vulnerability of dark networks. For example, which actor or set of actors should be targeted in order to dismantle and disrupt dark networks? Everton (2012b) has identified two main tasks for researchers interested in investigating strategies for network disruption: (1) exploratory SNA, which includes visualization (mapping) of dark networks; and (2) testing hypotheses (e.g., the predicted impact of law enforcement strategies) using mathematical algorithms to represent abstract parameters (e.g., roles or attributes of actors) and probabilities (e.g., of node removal). Many studies on dark networks have focused on the first, but few have addressed the second.
|Title of host publication||Illuminating Dark Networks|
|Publisher||Cambridge University Press|
|Number of pages||13|
|Publication status||Published - 2015|