  Detecting communities, densely connected groups may contribute to unravel the
underlying relationships among the units present in diverse biological networks
(e.g., interactome, coexpression networks, ecological networks, etc.). We
recently showed that communities can be very precisely characterized by
maximizing Surprise, a global network parameter. Here we present SurpriseMe, a
tool that integrates the outputs of seven of the best algorithms available to
estimate the maximum Surprise value. SurpriseMe also generates distance
matrices that allow to visualize the relationships among the solutions
generated by the algorithms. We show that the communities present in small and
medium-sized networks, with up to 10.000 nodes, can be easily characterized: on
standard PC computers, these analyses take less than an hour. Also, four of the
algorithms may quite rapidly analyze networks with up to 100.000 nodes, given
enough memory resources. Because of its performance and simplicity, SurpriseMe
is a reference tool for community structure characterization.
