title: SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization.
abstract: CONCLUSIONS
  Detecting communities and densely connected groups may contribute to unravel the underlying relationships among the units present in diverse biological networks (e.g. interactomes, coexpression networks, ecological networks). We recently showed that communities can be 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 visualizing 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 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.
  
  
  METHODS
  SurpriseMe is implemented in Perl and C/C++. It compiles and runs on any UNIX-based operating system, including Linux and Mac OS/X, using standard libraries. The source code is freely and publicly available under the GPL 3.0 license at http://github.com/raldecoa/SurpriseMe/releases.
authors.affiliation: Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas (IBV-CSIC), Valencia 46010, Spain.
authors.forename: Rodrigo
authors.initials: R
authors.lastname: Aldecoa
authors.forename: Ignacio
authors.initials: I
authors.lastname: Marín
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D011381:Programming Languages; D012984:Software; D013997:Time Factors