On Mon, May 5, 2014 at 1:06 PM, Tim Althoff <[email protected]> wrote:
> Hi, > > I am performing community detection on citation network graphs (~20k > nodes). It seems like all (most?) community detection algorithms are based > on modularity which according to this paper ( > http://dl.acm.org/citation.cfm?id=2350193) is a bad idea. They propose > conductance (or e.g. triangle participation ratio) as a metric to optimize > for communities. In particular I am interested in a score for maximum > community saliency (or e.g. minimum conductance cut). > > Does iGraph have such capabilities? I could find anything about > conductance in the docs. > > I believe the Stanford SNAP library has similar functionality (C++) but I > would prefer staying with Python if possible. > > Any comments and ideas are very welcome! > How about this: http://snap.stanford.edu/snappy/ ? G. > > Thanks, > Tim > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > >
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