I'm wondering if someone could suggest what are good starting points when it comes to performing community detection/graph partitioning/clustering on a graph that has weighted, undirected edges. The graph in question has approximately 3 million edges and each edge expresses the degree of similarity between the two vertices it connects. In particular, in this dataset edges are individuals and vertices are a measure of the similarity of their observed behavior.
In the past I followed a suggestion I got here on stats.stackexchange.com and used igraph's implementation of Newman's modularity clustering and was satisfied with the results, but that was on a unweighted dataset.
Are there any specific algorithms I should be looking at?
Many thanks for any advice ~l