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, > b, N), edge_probs = prob) > > However, this generates an ER graph. What can I do to retain the > block-structure?
I believe this is a bug with the alias method used. Try with the option "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 <[email protected]>
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