Thank you so much for your answer, how would you scale the graph size ?my graph is not a large graph but it is pretty big right? (almost 20000 nodes and 200000 edges)
On Thu, Oct 16, 2014 at 1:27 PM, Tamas Nepusz <[email protected]> wrote: > as I have two types of graph one is directed an weighted and the other one > is undirected and unweighted, the one which I could use for both are four > (1,2,4,5) which I get the error on the forth one as my graph is an > unconnected graph, so there is three. > > You could still use the fourth one by decomposing your graph into > connected components first (see ?decompose.graph), calculating the > communities for each of the components, and then merging the community > membership vectors. > > > > - is there any other algorithm which is implemented in igraph and is > not in the list? and which will give me overlapping communities as well. > > No, but clique percolation is not particularly hard to implement in igraph > - the naive solution would work for graphs of moderate size: > > http://igraph.wikidot.com/community-detection-in-r#toc0 > > > > - > which of these metric could be used for weighted and directed graph > and is there any implementation in igraph? > > modularity works for weighted graphs but ignores edge directions (since > there is no agreement on the scientific community yet about how to extend > modularity for directed networks; several competing proposals have been > described in the literature). compare.communities() does not care about the > graph since it compares the communities with a ground truth, so it does not > matter whether the graph was directed or not. > > > > - > also which metric could be used for which algorithm? , as I go through > one of the article "edge-betweeness"the metric used in there was the ground > truth and they compare to the known community graph. > > You could use any of the metrics with any of the algorithms. Keep in mind > that some of the algorithms explicitly try to optimise the modularity > behind the scenes (one way or another). > > > T. > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > > -- regards F..A
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