Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Models for Computing Hierarchies in Information Networks   Information hierarchies are organizational structures that often used to
organize and present large and complex information as well as provide a
mechanism for effective human navigation. Fortunately, many statistical and
computational models exist that automatically generate hierarchies; however,
the existing approaches do not consider linkages in information {\em networks}
that are increasingly common in real-world scenarios. Current approaches also
tend to present topics as an abstract probably distribution over words, etc
rather than as tangible nodes from the original network. Furthermore, the
statistical techniques present in many previous works are not yet capable of
processing data at Web-scale. In this paper we present the Hierarchical
Document Topic Model (HDTM), which uses a distributed vertex-programming
process to calculate a nonparametric Bayesian generative model. Experiments on
three medium size data sets and the entire Wikipedia dataset show that HDTM can
infer accurate hierarchies even over large information networks.

26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin