  Certain crimes are hardly committed by individuals but carefully organised by
group of associates and affiliates loosely connected to each other with a
single or small group of individuals coordinating the overall actions. A common
starting point in understanding the structural organisation of criminal groups
is to identify the criminals and their associates. Situations arise in many
criminal datasets where there is no direct connection among the criminals. In
this paper, we investigate ties and community structure in crime data in order
to understand the operations of both traditional and cyber criminals, as well
as to predict the existence of organised criminal networks. Our contributions
are twofold: we propose a bipartite network model for inferring hidden ties
between actors who initiated an illegal interaction and objects affected by the
interaction, we then validate the method in two case studies on pharmaceutical
crime and underground forum data using standard network algorithms for
structural and community analysis. The vertex level metrics and community
analysis results obtained indicate the significance of our work in
understanding the operations and structure of organised criminal networks which
were not immediately obvious in the data. Identifying these groups and mapping
their relationship to one another is essential in making more effective
disruption strategies in the future.
