This question and answer (http://stats.stackexchange.com/questions/2948/how-to-do-community-detection-in-a-weighted-social-network-graph) shows that weighted community detection can be done in igraph.
But my question is how has the binary tie community detection technique been generalised to use edge weights?
From this paper:
"notice that any two vertices that are particularly strongly connected to one another will have a particularly short distance along the edge between them. Geodesic paths will thus, all other things being equal, prefer to ﬂow along such an edge than along another longer edge between two less well connected vertices, and hence closely connected pairs will tend to attract a lot of paths and acquire high betweenness. This means that, as a general rule, we are more likely to remove edges between well connected pairs than we are between poorly connected pairs, and this is the precise opposite of what we would like the algorithm to do."
Does igraph use weighted edgebetweeness or does sit use the different implementation as suggested by newman:
" Another and simpler way of summarizing this algorithm is the following: we calculate the betweennesses of all edges in our weighted graph in the normal way, ignoring the weights. Then we divide each such betweenness by the weight of the corresponding edge, remove the edge with the highest resulting score, recalculate the betweennesses, and repeat."