BioinformaticsArchive/SurpriseMe
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* SurpriseMe - An integrated tool for network community structure characterization using Surprise maximization * ---------------------------------------------------------------------- SurpriseMe is a free, open source software for detecting community structure in networks. The program uses Surprise to evaluate the outputs of 7 high- quality community detection algorithms. It also generates distance matrices that allow to visualize the relationships among the solutions generated. You can find more information here: arxiv.org/abs/1310.2357 License ======= Copyright (C) 2013 Rodrigo Aldecoa and Ignacio Marín This software is distributed under the GNU General Public License version 3.0 (GPLv3). You should have received a copy of this license with the program. Also, it can be found online at http://www.opensource.org/licenses/gpl-3.0.html. Version ======= SupriseMe 1.0 Interesting related papers ========================== For more information about Surprise, please check out: * "Deciphering network community structure by Surprise". Rodrigo Aldecoa and Ignacio Marín. PloS ONE 6, e24195 (2011). http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0024195 and: * "Surprise maximization reveals the community structure of complex networks". Rodrigo Aldecoa and Ignacio Marín. Scientific Reports 3, 1060 (2013). http://www.nature.com/srep/2013/130114/srep01060/full/srep01060.html Programs included in this distribution ====================================== The following algorithms are included in SurpriseMe. If you use their solutions in your publications, please cite them appropriately: CPM: "Narrow scope for resolution-limit-free community detection" Traag VA, Van Dooren P, Nesterov Y Phys. Rev. E 84, 016114 (2011) Infomap: "Maps of random walks on complex networks reveal community structure" Rosvall M, Bergstrom CT Proc. Natl. Acad. Sci. USA 105, 1118 (2008) RB: "Statistical mechanics of community detection" Reichardt J, Bornholdt S Phys. Rev. E 74, 016110 (2006) RN: "Local resolution-limit-free Potts model for community detection" Ronhovde P, Nussinov Z Phys. Rev. E 81, 046114 (2010) RNSC: "Protein complex prediction via cost-based clustering" King AD, Przulj N, Jurisica I Bioinformatics 20, 3013 (2004) SCluster: "Jerarca: efficient analysis of complex networks using hierarchical clustering" Aldecoa R, Marin I Plos ONE 5, e11585 (2010) UVCluster: "Iterative cluster analysis of protein interaction data" Arnau V, Mars S, Marin I Bioinformatics 21, 364 (2005) (and also the same reference as SCluster) Compilation =========== Note: The code should compile in any Linux distribution using its basic, standard libraries (g++, make...). Unzip the tar.gz file and enter the directory: tar -xvzf SurpriseMe.tar.gz cd SurpriseMe Build and compile the program: make Usage ===== Once the program is compiled, just run: ./SurpriseMe.pl networkFile where networkFile is the file containing the network to be analyzed. Each line of network_file should be of the form id_1 id_2 if node with id_1 is connected with node with id_2. Links are assumed bidirectional, so that if id_1 id_2 is present in networkFile then id_2 id_1 should not be present. Some examples of this format can be seen in the net_examples folder. Example: ./SurpriseMe net_examples/sample.pairs Output files ============= The execution of the program creates the following files: - networkFile.S Contains the value of Surprise for the solution of each algorithm. - networkFile_algorithm.part Partition obtained by an algorithm. Each line contains the name of a node and its corresponding community - networkFile_VI.meg MEGA (www.megasoftware.net) input file. Contains the distance matrix of all solutions. Uses the Variation of Information as a distance between partitions. - networkFile_1-NMI.meg MEGA (www.megasoftware.net) input file. Contains the distance matrix of all solutions. Uses (1 minus the Normalized Mutual Information) as a distance between partitions. How to run only certain algorithms ================================== You can modify line 27 in SurpriseMe.pl in order to run only a few algorithms. By default, the program runs all of them. Just remove from the array @algs those you want to exclude from the analysis. Resources. Report bugs or suggestions ===================================== * Homepage: https://github.com/raldecoa/SurpriseMe * Email: Rodrigo Aldecoa <raldecoa (at) ibv.csic.es> Ignacio Marín <imarin (at) ibv.csic.es>
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