Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Machine Learning and Social Network Analysis to Reveal the
  Organizational Structures   Formation of a hierarchy within an organization is a natural way of assigning
the duties, delegating responsibilities and optimizing the flow of information.
Only for the smallest companies the lack of the hierarchy, that is, a flat one,
is possible. Yet, if they grow, the introduction of a hierarchy is inevitable.
Most often, its existence results in different nature of the tasks and duties
of its members located at various organizational levels or in distant parts of
it. On the other hand, employees often send dozens of emails each day, and by
doing so, and also by being engaged in other activities, they naturally form an
informal social network where nodes are individuals and edges are the actions
linking them. At first, such a social network seems distinct from the
organizational one. However, the analysis of this network may lead to
reproducing the organizational hierarchy of companies. This is due to the fact
that that people holding a similar position in the hierarchy possibly share
also a similar way of behaving and communicating attributed to their role. The
key concept of this work is to evaluate how well social network measures when
combined with other features gained from the feature engineering align with the
classification of the members of organizational social network. As a technique
for answering this research question, machine learning apparatus was employed.
Here, for the classification task, Decision Trees, Random Forest, {Neural
Networks and Support Vector Machines} have been evaluated, as well as a
collective classification algorithm, which is also proposed in this paper. The
results demonstrate that the social network built using the metadata on
communication highly exposes the organizational structure.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio