  Extracting communities using existing community detection algorithms yields
dense sub-networks that are difficult to analyse. Extracting a smaller sample
that embodies the relationships of a list of suspects is an important part of
the beginning of an investigation. In this paper, we present the efficacy of
our shortest paths network search algorithm (SPNSA) that begins with an
"algorithm feed", a small subset of nodes of particular interest, and builds an
investigative sub-network. The algorithm feed may consist of known criminals or
suspects, or persons of influence. This sets our approach apart from existing
community detection algorithms. We apply the SPNSA on the Enron Dataset of
e-mail communications starting with those convicted of money laundering in
relation to the collapse of Enron as the algorithm feed. The algorithm produces
sparse and small sub-networks that could feasibly identify a list of persons
and relationships to be further investigated. In contrast, we show that
identifying sub-networks of interest using either community detection
algorithms or a k-Neighbourhood approach produces sub-networks of much larger
size and complexity. When the 18 top managers of Enron were used as the
algorithm feed, the resulting sub-network identified 4 convicted criminals that
were not managers and so not part of the algorithm feed. We also directly
tested the SPNSA by removing one of the convicted criminals from the algorithm
feed and re-running the algorithm; in 5 out of 9 cases the left out criminal
occurred in the resulting sub-network.
