Description Usage Arguments Details Author(s) References

The routine samples correctly from the null model of a given undirected graph creating a set of randomized version of the initial undirected graph.

1 2 | ```
birewire.sampler.undirected(adjacency,K,path,max.iter="n", accuracy=0.00005,
verbose=TRUE,MAXITER_MUL=10,exact=FALSE,write.sparse=TRUE)
``` |

`adjacency` |
Adjacency matrix of the initial undirected graph. Since 3.6.0 this matrix can contain also NAs and the position of such entries will be preserved by the SA; |

`K` |
The number of networks that has to be generated; |

`path` |
The directory in which the routine stores the outputs; |

`max.iter` |
"n" (default) see |

`accuracy` |
0.00005 (default) is the desired level of accuracy reflecting the average distance between the Jaccard index at the N-th step and its analytically derived fixed point in terms of fracion of common edges; |

`verbose` |
TRUE (default). When TRUE a progression bar is printed during computation. |

`MAXITER_MUL` |
10 (default). If |

`exact` |
FALSE (default). If TRUE the program performs |

`write.sparse` |
TRUE (default). If FALSE the table is written as an R data.frame (long time and more space needed) |

The routine creates, starting from the given path, different subfolders in order to have maximum 1000 files for folder . Moreover the incidence matrices are saved using `write_stm_CLUTO`

(sparse matrices) that can be loaded using `read_stm_CLUTO`

. The set is generated calling birewire.rewire.undirected on the last generated graph starting from the input graph.

Andrea Gobbi: <gobbi.andrea@mail.com>

Gobbi, A. and Iorio, F. and Dawson, K. J. and Wedge, D. C. and Tamborero, D. and Alexandrov, L. B. and Lopez-Bigas, N. and Garnett, M. J. and Jurman, G. and Saez-Rodriguez, J. (2014) *Fast randomization of large genomic datasets while preserving alteration counts* Bioinformatics 2014 30 (17): i617-i623 doi: 10.1093/bioinformatics/btu474.

Iorio, F. and and Bernardo-Faura, M. and Gobbi, A. and Cokelaer, T.and Jurman, G.and Saez-Rodriguez, J. (2016) *Efficient randomization of biologicalnetworks while preserving functionalcharacterization of individual nodes* Bioinformatics 2016 1 (17):542 doi: 10.1186/s12859-016-1402-1.

Gobbi, A. and Jurman, G. (2013) *Theoretical and algorithmic solutions for null models in network theory* (Doctoral dissertation) http://eprints-phd.biblio.unitn.it/1125/

R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, U. Alon (2003), *On the uniform generation of random graphs with prescribed degree sequences*, eprint arXiv:cond-mat/0312028

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