[graph-tool] Preserving the degree-sequence while generating SBM

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? Thank you -- Snehal M. Shekatkar Pune India