Re: [igraph] community detection significance

[Tamás Nepusz]

Dear Filipe, 

None of the community significance algorithms are implemented in igraph yet and 
I am not aware of any such implementations yet. A relatively simple approach is 
to take each vertex in the community, calculate their internal and external 
degrees (i.e. the number of edges incident on them that lead inside the 
community and outside the community, respectively), and then run a Mann-Whitney 
U-test. The null hypothesis is that the distribution of internal and external 
degrees is the same (in other words, the community is not a real one because it 
is just as dense "inside" as "outside"), and the test will reject it if the 
community is a significant one. I'm sure that there are far better methods than 
this, but this is relatively easy to implement both in R and in Python using 
igraph. 

Best,-- 
T.


On Thursday, 14 June 2012 at 20:55, Filipe Alberto wrote:

> Dear list,
> 
> I have been using igraph to detect communities in networks. However, I would 
> like to use some method to get significance of the derived communities. I see 
> that there are several algorithms published, but I don't know if they are 
> available in igraph or if some of the users have implemented them using R and 
> igraph? 
> 
> 
> Thanks you for your help,
> 
> All the best,
> Filipe Alberto
> 
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