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From: | Tamas Nepusz |
Subject: | Re: [igraph] community detection algorithm and Evaluation |
Date: | Thu, 16 Oct 2014 11:57:10 +0200 |
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.
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
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.
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. |
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