tive in tearing apart a pure scale-free
network (such as the Dark Web, with
a high degree-betweenness-rank-or-der correlation, 0.70) in which hubs
function simultaneously as bridges
connecting different parts of the network.
Dark networks (such as those involving
terrorists and criminal narcotics traffickers) are hidden from nonparticipants yet could have a devastating effect on our social order and economy.
Understanding their topology yields
greater insight into the nature of clandestine organizations and could help
develop effective disruptive strategies. However, obtaining reliable data
about dark networks is extremely difficult, so our understanding of them
remains largely hypothetical. To the
best of our knowledge, the data sets
we explore here, though subject to
limitations, are the first to allow for
statistical analysis of the topologies of
We found that the covert networks
we studied share many common topological properties with other types of
networks. Their efficiency in terms of
communication and information flow
and commands can be tied to their
small-world structures, which are
characterized by short average path
length and a high clustering coefficient. In addition, we found that due
to their small-world properties, dark
networks are more vulnerable to attack on their bridges that connect different communities within them than
to attacks on their hubs. This finding
may give authorities insight for intelligence and security purposes.
Another interesting finding about
the three elicited human networks we
studied is that their substantially high
clustering coefficients (not always
present in other empirical networks)
are difficult to regenerate based on
only known network effects (such as
preferential attachment and small-world effects). Other mechanisms
(such as recruitment) may also play an
important role in network evolution.
Other research has found that alternative mechanisms (such as highly
optimized tolerance) may govern the
evolution of many complex systems in
environments characterized by high
risk and uncertainty. 3 Our future research will focus on the effects of such
alternative mechanisms on network
topology. In addition, our findings are
to the best of our all based on a static view of the net- works we studied; that is, we did not
knowledge, the data consider a large variety of dynamics
sets we explore that might have taken place in the evo- lution of the networks, so evolution
here, though subject study is definitely in our plans for fu-
to limitations, are ture research. Please also note that care is needed
the first to allow for when interpreting these findings. Because dark networks are covert and
statistical analysis largely unknown, hidden links may
of the topologies be missing in the elicited networks. These links may play a critical role in
of dark networks. maintaining the function of the covert
organizations. As a result, one must be
extremely cautious when a decision is
to be made to disrupt them.
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Jennifer Xu ( firstname.lastname@example.org) is an assistant professor
of computer information systems in Bentley College,
hsinchun Chen ( email@example.com) is McClelland
Endowed Professor in the Department of Management
Information Systems and head of the Artificial
Intelligence Lab at the University of Arizona, Tucson, AZ.
© 2008 ACM 0001-0782/08/1000 $5.00