Thank you for your replies - the fastgreedy method seems to be the logical option, whilst still giving results consistent with my previous analysis, so I think I’ll proceed with that.
Thank you so much for your help, Many thanks Lucy On 20 Apr 2015, at 22:04, Fabio Daolio <[email protected]> wrote: > Dear Lucy, > > if the network is not too big, you could also try the “spinglass.community” > method > fixing the number of spins equal to the (maximum) number of partitions you > need. > > This does not ensure that you will get the optimal partitioning either, nor > that you > will actually get the number of partitions you want (it could be less), but > if you play > with the parameters (e.g. by setting a cooling factor very close to 1), you > should get > reasonably good results in terms of modularity. > > Best, > — > Fabio > >> On 20 Apr 2015, at 22:33, Tamas Nepusz <[email protected]> wrote: >> >> Dear Lucy, >> >>> I am running some modularity analysis, and would like to find the optimal >>> modularity of a network partition, whilst specifying the final number of >>> communities. For example, I would like to find the best partition of >>> a network into 8 communities with the corresponding optimal modularity >>> score. >> Most modularity optimization algorithms (including all but one in igraph) are >> heuristics, which mean that the community structure that you get in the end >> is >> not necessarily optimal, and we can only expect that the end result is close >> to >> the "real" optimum. This is because finding the community structure that >> yields >> the highest possible modularity for a given network was shown to be >> computationally hard [1]. Since the optimality is not guaranteed for the end >> result of the algorithms, you can usually just take a community detection >> method that is hierarchical (e.g., the fast greedy method or the walktrap >> method) and cut the dendrogram at 8 communities if the method ended up with >> less than 8 communities in the end. Basically, you are stopping the community >> merging process before the method has reached its "optimum", which is not the >> true optimum anyway. >> >> [1] http://arxiv.org/abs/physics/0608255 >> >> The only exception to the above is the "optimal modularity" method in igraph, >> which, as its name implies, finds the community structure that maximizes the >> modularity score. Unfortunately this is computationally not feasible for most >> graphs -- it scales up only to a handful of vertices, so this is probably not >> applicable for your graphs. And even if it would be, you cannot specify the >> number of communities in that algorithm. >> >> All the best, >> -- >> T. >> >> _______________________________________________ >> 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] https://lists.nongnu.org/mailman/listinfo/igraph-help
