Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Inference for Stochastic Block Models from Sampled Data   This paper deals with non-observed dyads during the sampling of a network and
consecutive issues in the inference of the Stochastic Block Model (SBM). We
review sampling designs and recover Missing At Random (MAR) and Not Missing At
Random (NMAR) conditions for the SBM. We introduce variants of the variational
EM algorithm for inferring the SBM under various sampling designs (MAR and
NMAR) all available as an R package. Model selection criteria based on
Integrated Classification Likelihood are derived for selecting both the number
of blocks and the sampling design. We investigate the accuracy and the range of
applicability of these algorithms with simulations. We explore two real-world
networks from ethnology (seed circulation network) and biology (protein-protein
interaction network), where the interpretations considerably depends on the
sampling designs considered.

10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics