Description Usage Arguments Details Value Author(s) References See Also Examples
sim.rand.graph.par
simulates N
simple random graphs with the
same clustering (optional) and degree sequence as the input. Essentially a
wrapper for sample_degseq
(or, if you want to match by
clustering, sim.rand.graph.clust
) and
set_brainGraph_attr
. It uses foreach
for
parallel processing.
sim.rand.graph.clust
simulates a random graph with a given degree
sequence and clustering coefficient. Increasing the max.iters
value will result in a closer match of clustering with the observed graph.
1 2 3 4  sim.rand.graph.par(g, N = 100, clustering = FALSE, ...)
sim.rand.graph.clust(g, rewire.iters = 10000, cl = g$transitivity,
max.iters = 100)

g 
An 
N 
Integer; the number of random graphs to simulate (default: 100) 
clustering 
Logical; whether or not to control for clustering (default:

... 
Other parameters (passed to 
rewire.iters 
Integer; number of rewiring iterations for the initial graph randomization (default: 1e4) 
cl 
The clustering measure (default: transitivity) 
max.iters 
The maximum number of iterations to perform; choosing a lower number may result in clustering that is further away from the observed graph's (default: 100) 
If you do not want to match by clustering, then simple rewiring of the input
graph is performed (the number of rewire's equaling the larger of 1e4
and 10 \times m, where m is the graph's edge count).
sim.rand.graph.par
 a list of N random graphs
with some additional vertex and graph attributes
sim.rand.graph.clust
 A single igraph
graph object
Christopher G. Watson, [email protected]
Bansal S., Khandelwal S., Meyers L.A. (2009) Exploring biological network structure with clustered random networks. BMC Bioinformatics, 10:405421.
rewire, sample_degseq,
keeping_degseq
Other Random graph functions: RichClub
,
analysis_random_graphs
1 2 3 4 5 6  ## Not run:
rand1 < sim.rand.graph.par(g[[1]][[N]], N=1e3)
rand1.cl < sim.rand.graph.par(g[[1]][[N]], N=1e2,
clustering=T, max.iters=1e3)
## End(Not run)

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