birewire.rewire.dsg: Efficient rewiring of directed signed graphs

In BiRewire: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals)

Description

Optimal implementation of the switching algorithm. It returns the rewired version of the initial directed signed graph (dsg).

Usage

birewire.rewire.dsg(dsg,exact=FALSE,verbose=1,max.iter.pos='n',max.iter.neg='n',
  accuracy=0.00005,MAXITER_MUL=10,path=NULL,delimitators=list(positive='+',negative= '-'))

Arguments

dsg	A dsg object: is a list of two incidence matrices (see References), "positive" and "negative", encoding the positive edges and negative edges. This list can be obtained reading a SIF file using birewire.load.dsg function and converting the resulting dataframe using birewire.induced.bipartite;
exact	FALSE (default). If TRUE the program performs max.iter successful swithcing steps, otherwise the program will count also the not-performed swithcing steps;
verbose	TRUE (default). When TRUE a progression bar is printed during computation;
max.iter.pos	"n" (default) the number of switching steps to be performed on the positive part of dsg (or if exact==TRUE the number of successful switching steps). If equal to "n" then this number is considered equal to the analytically derived lower bound presented in Gobbi et al. (see References): N={e}/{2(1-d)} \ln{((e-de)/δ)} if exact is FALSE, N={e(1-d)}/{2} \ln{((e-de)/δ)} otherwise , where e is the number of edges of g and d its edge density . This bound is much lower than the empirical one proposed in Milo et al. 2003 (see References);
max.iter.neg	"n" (default) the number of switching steps to be performed on the negative part of dsg (or if exact==TRUE the number of successful switching steps). If equal to "n" then this number is considered equal to the analytically derived lower bound presented in Gobbi et al. (see References): N={e}/{2(1-d)} \ln{((e-de)/δ)} if exact is FALSE, N={e(1-d)}/{2} \ln{((e-de)/δ)} otherwise , where e is the number of edges of g and d its edge density . This bound is much lower than the empirical one proposed in Milo et al. 2003 (see References);
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;
MAXITER_MUL	10 (default). If exact==TRUE in order to prevent a possible infinite loop the program stops anyway after MAXITER_MUL*max.iter iterations;
path	NULL (default). If not NULL, the dsg is saved in path in SIF format;
delimitators	list(positive='+',negative= '-') (default). If save.file is true, the dsg is saved using delimitators as characters encoding the relations. See birewire.build.dsg for more details.

Details

This fuction runs birewire.rewire.bipartite on the positive and negative part of dsg. See references for more details.

Value

Rewired dsg.

Author(s)

Andrea Gobbi: <[e-mail]>

References

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 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.

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

Examples

library(BiRewire)
data(test_dsg)
dsg=birewire.induced.bipartite(test_dsg)
tmp= birewire.rewire.dsg(dsg,verbose=FALSE)

BiRewire documentation built on Nov. 8, 2020, 8:09 p.m.