Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Bayesian Inference and Complexity Control for Stochastic
  Block Models   It is now widely accepted that knowledge can be acquired from networks by
clustering their vertices according to connection profiles. Many methods have
been proposed and in this paper we concentrate on the Stochastic Block Model
(SBM). The clustering of vertices and the estimation of SBM model parameters
have been subject to previous work and numerous inference strategies such as
variational Expectation Maximization (EM) and classification EM have been
proposed. However, SBM still suffers from a lack of criteria to estimate the
number of components in the mixture. To our knowledge, only one model based
criterion, ICL, has been derived for SBM in the literature. It relies on an
asymptotic approximation of the Integrated Complete-data Likelihood and recent
studies have shown that it tends to be too conservative in the case of small
networks. To tackle this issue, we propose a new criterion that we call ILvb,
based on a non asymptotic approximation of the marginal likelihood. We describe
how the criterion can be computed through a variational Bayes EM algorithm.

1
The categories of this paper are: cs
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol