  We study social networks and focus on covert (also known as hidden) networks,
such as terrorist or criminal networks. Their structures, memberships and
activities are illegal. Thus, data about covert networks is often incomplete
and partially incorrect, making interpreting structures and activities of such
networks challenging. For legal reasons, real data about active covert networks
is inaccessible to researchers. To address these challenges, we introduce here
a network generator for synthetic networks that are statistically similar to a
real network but void of personal information about its members. The generator
uses statistical data about a real or imagined covert organization network. It
generates randomized instances of the Stochastic Block model of the network
groups but preserves this network organizational structure. The direct use of
such anonymized networks is for training on them the research and analytical
tools for finding structure and dynamics of covert networks. Since these
synthetic networks differ in their sets of edges and communities, they can be
used as a new source for network analytics. First, they provide alternative
interpretations of the data about the original network. The distribution of
probabilities for these alternative interpretations enables new network
analytics. The analysts can find community structures which are frequent,
therefore stable under perturbations. They may also analyze how the stability
changes with the strength of perturbation. For covert networks, the analysts
can quantify statistically expected outcomes of interdiction. This kind of
analytics applies to all complex network in which the data are incomplete or
partially incorrect.
