I am working with about 300 disconnected of different sizes. I calculate different graph-level centralization measures for these networks using the STATNET and iGraph packages in R.
However, I find that the nodes in subgraphs of N=2 get assigned the highest value of 1 for the Eigenvector centrality measure with iGraph. As a result, networks with a lot of isolated dyads get very high graph-level Eigenvector centralization scores.
In my networks this is not a valid result, because these networks are poorly connected and thus should, theoretically, have a low centralization score.
Does anyone know how these measures handle disconnected graphs? And are there ways to deal with this? Also, are there other ways to assess the structure of these networks?
Any help is welcome. Thank you!