That worked! Thanks a lot. Snehal
On Mon, Oct 2, 2017 at 9:54 PM, Tiago de Paula Peixoto <[email protected]> wrote: > 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]> > > > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool > > -- Snehal M. Shekatkar Pune India
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