contributed articles
Doi: 10.1145/1400181.1400198
Knowing the structure of criminal and terrorist
networks could provide the technical insight
needed to disrupt their activities.
By JennifeR xu anD hsinchun chen
the topology
of Dark
networks
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 scientific 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-trafficking 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 network 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 topology; three of which—average path
length, average clustering coefficient,
and degree distribution—are widely
used to categorize networks.
In random networks, two arbitrary
nodes are connected with a probability p; as a result each node has roughly
the same number of links. Random
networks are characterized by small
l, small C, and bell-shaped Poisson
distributions. 1 A small l means an arbitrary node can reach any other node
in a few steps. A small C implies that
random networks are not likely to
contain clusters and groups. Studies
by physicists and computer and social
scientists have found that most complex systems are not random but present small-world and scale-free properties (see Albert1 for a comprehensive
review of these studies).
A small-world network has a significantly larger C than its random-network counterpart while maintaining a relatively small l. 11 Scale-free
networks, on the other hand, are char-
ILLUS TRATION B Y LEANDER HERZOG