Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Community Detection through Likelihood Optimization: In Search of a
  Sound Model   Community detection is one of the most important problems in network
analysis. Among many algorithms proposed for this task, methods based on
statistical inference are of particular interest: they are mathematically sound
and were shown to provide partitions of good quality. Statistical inference
methods are based on fitting some random graph model (a.k.a. null model) to the
observed network by maximizing the likelihood. The choice of this model is
extremely important and is the main focus of the current study. We provide an
extensive theoretical and empirical analysis to compare several models: the
widely used planted partition model, recently proposed degree-corrected
modification of this model, and a new null model having some desirable
statistical properties. We also develop and compare two likelihood optimization
algorithms suitable for the models under consideration. An extensive empirical
analysis on a variety of datasets shows, in particular, that the new model is
the best one for describing most of the considered real-world complex networks
according to the likelihood of observed graph structures.

6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg