Hi all,
I am looking for a way to generate random weighted graphs from an input graph that I already have. To tell you why, I am trying to find out all the vertices in my edge weighted graph with a high betweeenness centrality. For this what I am planning is to bootstrap my original graph, generate random networks from it, calculate betweeenness centrality, generate empirical p-values for each node based on the betweeenness centrality from the original network and random network. I tried using rewire.edges from R igraph 0.6 package, but found out that the generated random graphs had almost the same edge weights as that of my
original graph. Then I found this thread ( http://lists.gnu.org/archive/html/igraph-help/2012-07/msg00097.html ) from igraph mailing list, explaining how to generate random weighted graphs. But here, a small problem I had was that the reply said "decide whether there is any correlation
between the network structure and the weights", and right now I don't know how to estimate this. I tried googling to find some answers, but didn't reach anywhere. So, if someone could point out how to estimate if there is a correlation between network structure and weights, that would be of great help. Additionally, is there a better method to estimate the statistical significance of node betweenness centrality of weighted graphs ? any suggestions on that will also be of great help.
Thank
you
Sudeep