I think it will depend mostly on what is common in your field. I think generating a large set of equivalent random networks is generally OK to do. If you are doing group comparisons, permutation/randomization testing would be better, and bootstrapping
On Fri, Jul 3, 2015 at 8:16 PM, Manuel J. Zetina-Rejón <[email protected]> wrote: > Hi everyone > > Graph theory is not my expertise area. I’m ecologist working with food > webs (networks). I’m usually test hypothesis using non-parametric > statistical tests. During the lat year I start using random network models > to try to figure out if any result igraph is meaningful, for example, when > determining centrality indexes or when identifying communities, I run > Erdos-Reyni with same number of vertices and edges to test if my result is > not due to chance. I also use degree.sequence.game( ) similarly. > > In your opinion guys, do you think this is enough for testing statistical > significance? > > > Thank you, > > Manuel > > > Ps. Probably this is a widely question or not in the appropriate email > list, sorry. But if there any suggestion of additional tests, I expect to > run with igraph > > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help >
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