Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Oriented and Degree-generated Block Models: Generating and Inferring
  Communities with Inhomogeneous Degree Distributions   The stochastic block model is a powerful tool for inferring community
structure from network topology. However, it predicts a Poisson degree
distribution within each community, while most real-world networks have a
heavy-tailed degree distribution. The degree-corrected block model can
accommodate arbitrary degree distributions within communities. But since it
takes the vertex degrees as parameters rather than generating them, it cannot
use them to help it classify the vertices, and its natural generalization to
directed graphs cannot even use the orientations of the edges. In this paper,
we present variants of the block model with the best of both worlds: they can
use vertex degrees and edge orientations in the classification process, while
tolerating heavy-tailed degree distributions within communities. We show that
for some networks, including synthetic networks and networks of word
adjacencies in English text, these new block models achieve a higher accuracy
than either standard or degree-corrected block models.

15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an