SCIENTISTS FROM A variety of disciplines, including physics, sociology, biology, and computing, all explore the topological properties of complex systems that can be characterized as large-scale networks, including scienti c collaborations, the Web, the Internet, electric power grids, and biological and social networks. Despite the differences in their components, functions, and size, they are surprisingly similar in topology, leading to the conjecture that many complex systems are governed by the ubiquitous “self-organizing” principle, or that the internal complexity of a system increases without being guided or managed by external sources.

Still missing from this line of research, however, is an analysis of the topology of “dark” networks hidden from view yet that could have devastating effects on our social order and economy. Terrorist organizations, drug-traf cking rings, arms-smuggling operations, gang enterprises, and many other covert networks are dark networks. Their structures are largely unknown to outsiders due to the difficulty of accessing and collecting reliable data. Do they share the same topological properties as other types of empirical networks? Do they follow the self-organizing principle? How do they achieve efficiency under constant surveillance and threat from the authorities? How robust are they against attack? Here, we explore the topological properties of several covert criminal- and terrorist-related networks, hoping to contribute to the general understanding of the structural properties of complex systems in hostile environments while providing authorities insight regarding disruptive strategies.

Topological analysis focusing on the statistical characteristics of net- work structure is a relatively new methodology for studying large-scale networks.1,11 Large complex networks can be categorized into three types: random, small-world, and scale-free.1 A number of statistics (see Table 1) have been developed to study their to- pology; three of which—average path length, average clustering coef cient, and degree distribution—are widely used to categorize networks.

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