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From: | Gang Su |
Subject: | Re: [igraph] Assortativity in igraph-6.0 under R |
Date: | Wed, 19 May 2010 14:27:02 -0400 |
User-agent: | Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.9) Gecko/20100317 Thunderbird/3.0.4 |
--------- Hi Gabor,The authors used a two-step algorithm which includes 1. recursive partition. 2. refinement. The refinement part is independent of the community algorithm. And actually this scheme can be applied to any community algorithm which can work on disconnected graphs (also we can have a fix to let it work on connected graphs).
It gives smaller communities for larger networks.Actually it's not very difficult to implement this 2-step procedure in R, but it won't be very efficient. So I was wondering actually instead of Q-cut, do you have a plan for implementation of such multi-step procedures. I can be very helpful because it's generic, and can actually work on any graph partitioning algorithm.
Gang On 5/19/2010 10:47 AM, Gábor Csárdi wrote:
I think what you want to compare isassortativity(g1,degree(g1, mode="out")-1, degree(g1, mode="in")-1, directed=TRUE)[1] -0.3131201assortativity.degree(g1, directed=TRUE)[1] -0.3131201 because this is how assortativity.degree() works if directed=TRUE. Best, Gabor On Wed, May 19, 2010 at 4:36 PM, Przemek Grabowicz <address@hidden> wrote:Thank you for implementing assortativity in igraph-0.6, it's nice addition. However there is something there what I don't understand. Assortativity.degree gives different results than assortativity itself. If I understand manual properly it should give the same values.assortativity(g1,degree(g1)-1,directed=TRUE)[1] 0.1368699assortativity.degree(g1,directed=TRUE)[1] 0.1546312 It works fine when directed=FALSE. I'm using igraph_nightly_0.6-1899-20100505 under R-2.8.1 . _______________________________________________ igraph-help mailing list address@hidden http://lists.nongnu.org/mailman/listinfo/igraph-help
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