[igraph] community detection algorithms

[Larson, TR]

Hello,
My first post!  I have been experimenting with community detection 
algorithms in igraph (in R) as a way to extract groups of related 
chemical compounds.  Basically, I start with  2-column matrix of 
vertices several tens of thousands rows long with  ~ 400 unique 
vertices. Each row represents a pairing that has passed some previous 
association tests.

I then create an undirected graph from the matrix, stored 
count.multiple() into the graph weight attribute, and then simplify().

I then tried several community detection algorithms to return membership 
(either directly or via community.to.membership(), as appropriate):

label.propagation.community
walktrap.community
fastgreedy.community

I found the best algorithm to be label.propagation (in terms of 
separating what I know to be different clusters of chemically different 
compounds from each other, but on the other hand not falsely dividing a 
cluster containing highly related chemicals in a test set).

Walktrap and fastgreedy give the same membership results every time I 
run the algorithms. However, label.propagation.community it gives 
slightly different groupings each time. I have tried setting initial to 
a set vector (either all 0 or 0:(length(V(g)$name)-1)) and fixed to a 
vector of all FALSE values in case the algorithm results vary because of 
random starting values. However, this has no effect.

Any comments would be most welcome; also any other suggestions on how to 
extract related clusters from my graph structure.

thanks
Tony