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The documentation of minimize_blockmodel_dl says

See peixoto-hierarchical-2014 for details on the algorithm.

However, the paper explicitely states

However, in order to perform model selection, one first needs to find optimal partitions of the network for given values of B, which is the subproblem which we consider in detail in this work. Therefore, in the remainder of this paper we will assume that the value of B is a fixed parameter, unless otherwise stated, but the reader should be aware that this value itself can be determined at a later step via model selection, as described, e.g., in Refs. [19,26].

Hence, how exactly do minimize_blockmodel_dl and variants decide B? Ultimatively, I'd be interested in plotting the implied likelihoods for different values of B, but would first see what the algorythm has built-in by default - Bayesian model selection?

FooBar
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  • Dear downvoter, please explain what's missing.. to me, the question is clearly phrased, and I've tried to do my own research (there's just no more layer left after going to the paper linked in the documentation...) – FooBar May 23 '17 at 15:07
  • I don't know why somebody downvoted this question. +1 – Peaceful May 24 '17 at 07:31

1 Answers1

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You are confusing two different papers. The quote you show does not come from the paper you mention. The cited paper:

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.011047

explains exactly your question, i.e. how the most appropriate number of groups is determined, using minimum description length. You can also read a more recent introduction to Bayesian inference of the stochastic block model, which deals with this issue at length:

https://arxiv.org/abs/1705.10225

Tiago Peixoto
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