Re: [igraph] Suggestion for a new community detection function

[Gang Su]

Okay, there's actually another paper you may want to read:
http://arxiv.org/abs/0704.3759

What the author says makes sense. I have tried on two different datasets:
One with 2k nodes, 10k edges, and the other with 10k nodes, 100k edges. 
Almost all algorithms based on Q give similar number of clusters on two 
different datasets. That is, the number of clusters given by each 
algorithm is relatively stable, for large datasets you are likely to get 
large clusters and you may need to run the algorithm iteratively to get 
smaller clusters.

Right now i have tried different algorithms, it seems that spinglass and 
ken wakita's method(not in igraph, taking consolidation ratio into 
account) works best on my dataset. walk trap and fast greedy works 
worst, edgebetweenness works somewhere in between but too slow, leading 
eigenvector never worked :((Doesn't converge even with 1 million 
iterations on all my datasets.)

Gang

Tamas Nepusz wrote:
> > Tamas, when you say to implement in python that means you are not 
> > going to modify the C backend?
> I'm not planning to implement it in C yet, only in Python - but not in 
> the next two weeks, I assume :( I can send you the source code when 
> (and _if_ ;)) I implemented it. In case there is substantial demand 
> for it and the algorithm gets widespread in the network research 
> community, I will give it a try and translate it to C.