Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluating network partitions through visualization   Network clustering requires making many decisions manually, such as the
number of groups and a statistical model to be used. Even after filtering using
an information criterion or regularizing with a nonparametric framework, we are
commonly left with multiple candidates with reasonable partitions. In the end,
the user has to decide which inferred groups should be regarded as informative.
Here we propose a visualization method that efficiently represents network
partitioning based on statistical inference algorithms. Our non-statistical
assessment procedure based on visualization helps users extract informative
groups when they cannot uniquely determine significant groups on the basis of
statistical assessments. The proposed visualization is also effective for use
as a benchmark test of different clustering algorithms.

29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat