Hello, I'm pretty sure that your edges are weighted because otherwise this would be an almost complete graph for which there is no sensible clustering structure anyway. So, I would start by plotting the distribution of edge weights and throwing away all but the most heavyweight edges, maybe keeping the top 0.1% only. igraph's clustering algorithms are designed for sparse graphs, so you would have to sparsify your graph anyway to do any meaningful analysis on it.
T. On Sun, Jul 17, 2016 at 8:08 PM, Jack Zellweger <[email protected]> wrote: > Hello all, > > I hope this email finds you well! > > The Problem: > I am having trouble with run time attempting to cluster a very large > complete graph of 44 800 nodes and 1 003 497 600 edges. > > The Question: > What is the best igraph function to cluster such a large graph while > maintaining a good clustering reflecting the true modularity of the graph? > > Thank you for your time. > > Best, > Jack Zellweger > LIGO Research > Kenyon College > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help > _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
