  Analysis of criminal networks is inherently difficult because of the nature
of the topic. Criminal networks are covert and most of the information is not
publicly available. This leads to small datasets available for analysis. The
available criminal network datasets consists of entities, i.e. individual or
organizations, which are linked to each other. The links between entities
indicates that there is a connection between these entities such as involvement
in the same criminal event, having commercial ties, and/or memberships in the
same criminal organization. Because of incognito criminal activities, there
could be many hidden links from entities to entities, which makes the publicly
available criminal networks incomplete. Revealing hidden links introduces new
information, e.g. affiliation of a suspected individual with a criminal
organization, which may not be known with public information. What will we be
able to find if we can run analysis on a larger dataset and use link prediction
to reveal the implicit connections? We plan to answer this question by using a
dataset that is an order of magnitude more than what is used in most criminal
networks analysis. And by using machine learning techniques, we will convert a
link prediction problem to a binary classification problem. We plan to reveal
hidden links and potentially hidden key attributes of the criminal network.
With a more complete picture of the network, we can potentially use this data
to thwart criminal organizations and/or take a Pareto approach in targeting key
nodes. We conclude our analysis with an effective destruction strategy to
weaken criminal networks and prove the effectiveness of revealing hidden links
when attacking to criminal networks.
