Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Triangles as basis to detect communities: an application to Twitter's
  network   Nowadays, the interest given by the scientific community to the investigation
of the data generated by social networks is increasing as much as the
exponential increasing of social network data. The data structure complexity is
one among the snags, which slowdown their understanding. On the other hand,
community detection in social networks helps the analyzers to reveal the
structure and the underlying semantic within communities. In this paper we
propose an interactive visualization approach relying on our application
NLCOMS, which uses synchronous and related views for graph and community
visualization. Additionally, we present our algorithm for community detection
in networks. A computation study is conducted on instances generated with the
LFR [9]-[10] benchmark. Finally, in order to assess our approach on real-world
data, we consider the data of the ANR-Info-RSN project. The latter addresses
community detection in Twitter.

13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph