> the big connected component. Now edge.betweenness.community() reveals > some meaningful communities. Do you think such an approach is sound?
Well, as long as the communities are meaningful, then I guess this approach is sound. > Of course, your suggestion looks really interesting and I will give it > a try, but I have no knowledge of C++, so I don't know how to manage > such an analysis... should I use the C++ igraph? or can I call the C++ > code from R in a relatively easy way? Well, my code compiles to a command line application that takes the network in an edge list format, tries to fit a stochastic blockmodel to it, and then returns the classification of nodes into groups (where, say, group 1 = core and group 2 = periphery) and the connection probabilities within the core, within the periphery, and between the core and the periphery. So, there's no need for C++ coding once the code itself is compiled. As for R, you can call the executable from R using system() and then parse the output to get back the node classification as an R vector -- but you have to write this glue code yourself. Best, T. _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
