Hi Nico,

I haven’t written the code for a weighted bipartite network; however, there is nothing that would stop you from defining a rich club coefficient that incorporate weights, and then use the various reshuffling procedures for weighted bipartite networks.

Let me know how you get along!

Tore

Because now I get the error message: “There are duplicated entries in the edgelist” and since `weighted_richclub_tm` takes binary TM networks only, I cannot enter a weighted TM net (right?).

Hopefully you can help me out once more!

Nico

Thanks for your answer! I’ll try that :-)

]]>Hi Nico,

Glad you are interested in using the two-mode version of the weighted rich-club coefficient. I am not sure where that snippet of code is from; however, you should use the weighted_richclub_tm-function that computes the coefficient on an empirical network and compares it to ones found on an ensemble of reshuffled versions of the empirical network.

Best,

Tore

great blog, it’s been very interesting and usefull to me.

Unfortunately I ran into some trouble replicating this entry. I’m not sure, if entirely understand the approach to get the diagram with the normalised phi coefficient. So far I tried the following:

net <- as.tnet(edgelist, type="weighted two-mode tnet")

random_net <- rg_reshuffling_tm(net, option = "links", seed = 1)

random_one <- projecting_tm(random_net , method="sum")

out <- weighted_richclub_w(random_one, rich="k", reshuffle="links", NR=1000, seed=1)

Is this correct or should I rather reshuffle the two-mode-network 1000 times instead of the one mode rich club?

I hope you find the time to respond!

Nico