Monte Carlo or Bootstrapping for network in R I have a large network for which I'm using iGraph in R to handle.
However, I would like to take some small random samples of that network, and calculate for example the standard deviation of parallel edges weights, to see how much do they actually vary.
When looking at the data on a table format, it looks like this:
Origin  Destination  Weight
   A         B          30
   A         B          19
   A         C           1
   B         D          15
   B         D          40

Surely this is just a small example, but I wonder how could I do it using either iGraph or any other packages in R. I've been searching already for a while but I'm not sure how to. I assume that I would need either Bootstrapping or Monte Carlo methods for this, but code wise there isn't much info floating around.
EDIT: 
The idea behind what I want to do is well explained here:

observed data are resampled to create new datasets that match the size
  of the original data, while allowing the same observations to be drawn
  multiple times. This creates slightly different datasets each time,
  but always based on the same original observations. Repeating this
  process hundreds of times and re-calculating a given statistic for
  each new dataset generates a distribution of possible values. Lusseau
  et al. [9] suggested that this approach could be incorporated into
  social network analysis. In the case of networks, the observation data
  from which the observed network was generated is bootstrapped
  (observations are resampled, rather than resampling nodes) and a new
  network is generated for each dataset by re-calculating all the edge
  weights in exactly the same way. The statistic of interest in the
  observed network is re-calculated each time and recorded. The 95%
  confidence interval can then be inferred by extracting the 2.5% and
  97.5% quantiles of the recorded values.

(Estimating uncertainty and reliability of social network data using Bayesian inference - R. Farine et al.)
 A: I'm working on a similar problem and hoping for a similar approach.
So far I have little progress but it seems 'bootnet' is an option.
library(igraph)
library(bootnet)

g1 <- sample_pa_age(...., pa.exp=..., aging.exp=...., aging.bin=1000)
g2 <- as_data_frame(g1, what="edges")
results <- bootnet(g2, nBoots=1000, statistics = "betweenness", default = "EBICglasso", sampleSize=500, type = "nonparametric")

BUT I still need to contact 'bootnet' as I am having different problems with this approach. BUT at least, "a bootstrap" was implemented.
Pls update me if you have a better solution or what happened with your bootnet results. 'Would want to know if we have similar issues with bootnet.
A: I have a similar problem. As far as I know, the idea of the Monte Carlo method applied to networks is to allow simulating networks with certain parameters (for example, number of vertices and edges) and then calculate certain statistics of interest to compare their distribution against the observed measures. This is very different to Bootstrap. Some graph generators are available in Igraph: sample_gnp, sample_gnm, sample_pa, sample_smallworld.
But I don't know how to calculate and get the metrics in each network in a unified way to get the percentiles of the estimates (as confidence intervals).
