# Bias correction of a sampled igraph

Suppose I have a sample (S) of a graph where S is a subset of G -- the population Graph. Is there a way (theoretical) to compute for bias in the estimation of centralization (as in igraph::centr_degree(g)\$centralization) AND use this computed bias to correct my estimate?

Additional info: Suppose I used bootstrap graph samples (i.e. S*1,S*2, . . . ,S*n) and I know from my histogram of centralization from these samples that I am "far" from the population centralization. For illustration, a histogram of centralization is found below. Unfortunately, the population centralization is 0.011.

Now the problem here is the following. How to resample/rewire the graph. You can't do as in "normal" bootstrapping, because a sample of your population would "destroy" some statistics, so you have to work with reshuffles/rewires of your network. However, igraph offers a function for that in R called rewire which can be paired with keeping_degseq. What I couldn't understand so far and that's still a question here is what does the parameter niter exactly does. Once that's clear then it shouldn't be difficult to compute for bias estimation.