Network clustering stability using bootstrapping techniques I use the standard modularity-maximisation Louvain clustering method to partition a large undirected network into communities. I fear the result of the partition is quite fragile.
Is there a standard bootstrapping methodology to get a sense of the precision of my estimate in R for an igraph objects?
Resources I found that are partial solutions:
Bootstrapping networks with R and igraph
Bootstrap Evaluation of Clusters
GitHub/Skynet
GitHub/Snowboot
Package ‘DIscBIO’
Package ‘bootnet’
 A: You are right, modularity maximization is very unstable: just renumbering vertices, or removing/adding one edge may have a huge impact on obtained results. This was shown for instance in:
Static community detection algorithms for evolving networks
However, iterating community detection leads to very stable pieces in most practical cases:
Stable community cores in complex networks
but not in random graphs:
The Power of Consensus: Random Graphs Have No Communities
A: I don't know if there is a standard way, but here is a paper that discusses one approach. (DISCLAIMER: I know the authors, so take it as you will). There is an R package implementing the test and a description and examples of  how to apply it.
That paper also cites and compares with other approaches such as edge cross-validation and network cross-validation (as well as the spectral test, likelihood-based approaches and so on.) I suggest trying out the cross-validation approaches as well (there is an element of sampling to them). Those also have R code available.
