Ah I see, thank you Szabolcs! I am using several clustering methods available in igraph at once to compare outputs, so this is completely inconsistent between methods!!!
cluster_edge_betweenness - uses NULL to omit edge weights cluster_fast_greedy - uses NULL to omit edge weights (though it is not clear on this point?) cluster_label_prop - uses NA, but in the ‘usage’ states NULL cluster_leading_eigen - uses NA, but in the ‘usage’ states NULL cluster_louvain - uses NA, but in the ‘usage’ states NULL cluster_optimal - uses NA, but in the ‘usage’ states NULL cluster_walktrap - does not provide any guidance, I assumed NULL was used cluster_spinglass - uses NA, but usage states NULL I am still quite new to R so perhaps the ‘usage’ is meant to be read differently, but I thought it meant NULL could be used as a meaningful input Please could this be fixed so there is a uniform and clear approach in the next update to igraph, as it is very confusing at present Thanks, Edmund > On 25 May 2017, at 10:48, Szabolcs Horvát <[email protected]> wrote: > > On 25 May 2017 at 19:43, Edmund Hunt <[email protected] <mailto:[email protected]>> > wrote: >> Hi Gabor, >> >> Thanks for your reply. >> >> Here are 4 different commands and their result, I guess I am just a bit >> confused how they relate to each other. >> >> The first two are using the cluster_leading_eigen alone, the second two use >> that command to find the communities and then the modularity function to get >> the modularity value out of it >> >> Would I be right in understanding that cluster_leading_eigen only uses the >> weights argument after the communities have been found - but then why does >> it return the same value below for the first two commands - and why is it >> different to the third command >> >> Thanks >> >>> cluster_leading_eigen(net, weights = E(net)$weight) >> IGRAPH clustering leading eigenvector, groups: 2, mod: 0.055 >> + groups: >> $`1` >> [1] "YV" "B" "P" >> >> >> >> $`2` >> [1] "DG" "V" >> >> >>> cluster_leading_eigen(net, weights = NULL) >> IGRAPH clustering leading eigenvector, groups: 2, mod: 0.055 >> + groups: >> $`1` >> [1] "YV" "B" "P" >> >> >> >> $`2` >> [1] "DG" "V" > > > According to the documentation, you need to supply weights=NA, and not > weights=NULL, to ignore any existing weight values in the graph. > >> >>> modularity(net,membership(cluster_leading_eigen(net, weights = >>> E(net)$weight)),weights=NULL) >> [1] 0.03061224 >> >>> modularity(net,membership(cluster_leading_eigen(net, weights = >>> E(net)$weight)),weights=E(net)$weight) >> [1] 0.0546875 >> >> >> >> On 25 May 2017, at 06:51, Gábor Csárdi <[email protected]> wrote: >> >> IIRC the original algorithm can be extended easily to take weights >> into account. >> >> If you think the igraph is not doing that (and the docs say that it >> would), can you please provide a small example that gives you the same >> results with or without (large enough) weights? Thanks. >> >> Gabor >> >> On Wed, May 24, 2017 at 10:11 AM, Edmund Hunt <[email protected]> wrote: >> >> Hello, >> >> I have a question/comment about the leading.eigenvector.community function >> in igraph >> >> It has an argument for weights, but this seems to make no difference to the >> calculated clusters/resulting modularity >> >> Indeed I don’t think Newman’s algorithm takes edge weights into account? >> >> Is it the case that the weights are only used after the community detection >> has taken place, to calculate a modularity value? Is it appropriate to use >> the weights to calculate modularity, can anyone advise me what is the >> ‘right’ thing to do with a weighted, undirected network - is it definitely >> to use the weights in the modularity calculation, or is there a free choice >> >> Perhaps these issues could be made clearer in the function help >> >> Thanks >> >> _______________________________________________ >> 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 >> >> >> >> _______________________________________________ >> igraph-help mailing list >> [email protected] <mailto:[email protected]> >> https://lists.nongnu.org/mailman/listinfo/igraph-help >> <https://lists.nongnu.org/mailman/listinfo/igraph-help> >> > > _______________________________________________ > igraph-help mailing list > [email protected] <mailto:[email protected]> > https://lists.nongnu.org/mailman/listinfo/igraph-help > <https://lists.nongnu.org/mailman/listinfo/igraph-help>
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