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Detecting communities, densely connected groups may contribute to unravel theunderlying relationships among the units present in diverse biological networks(e.g., interactome, coexpression networks, ecological networks, etc.). Werecently showed that communities can be very precisely characterized bymaximizing Surprise, a global network parameter. Here we present SurpriseMe, atool that integrates the outputs of seven of the best algorithms available toestimate the maximum Surprise value. SurpriseMe also generates distancematrices that allow to visualize the relationships among the solutionsgenerated by the algorithms. We show that the communities present in small andmedium-sized networks, with up to 10.000 nodes, can be easily characterized: onstandard PC computers, these analyses take less than an hour. Also, four of thealgorithms may quite rapidly analyze networks with up to 100.000 nodes, givenenough memory resources. Because of its performance and simplicity, SurpriseMeis a reference tool for community structure characterization.

SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization