I have a large igraph object 70,000+ vertices (nodes) and 200,000+ edges (connections). I'd like to calculate some measures of centrality but the network is too big. I thought a good work around would be to break my network into connected components (even the largest isn't too big).
I was thinking about using the igraph function clusters or related methods. Could I then calculate alpha.centrality() and bonpow() on the clusters? and then merge the results back into the original igraph object? (or a dataframe with all vertices)
I'm not sure of the best approach and I'd be really interested to hear any ideas people have. Many thanks :)