On 27.09.2017 03:57, Snehal Shekatkar wrote:
> Hello Tiago,
>
> I am trying to generate a stochastic block model but with the
> degree-sequence preserved. I am fine even if the degree-distribution is
> preserved instead of the exact sequence. I tried the following:
>
> def prob(a, b):
> if a == b :
> return 0.999
> else:
> return 0.001
>
> g, bm = gt.random_graph(N, lambda: 1 + np.random.poisson(5), model =
> "blockmodel-degree", directed = False, block_membership=np.random.randint(0, I believe this is a bug with the alias method used. Try with the option
> b, N), edge_probs = prob)
>
> However, this generates an ER graph. What can I do to retain the
> block-structure?
"alias=False", and don't forget to use a large value of "n_iter".
I'll provide a fix in git soon.
Best,
Tiago
--
Tiago de Paula Peixoto <tiago@skewed.de>
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