Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding Social Networks using Transfer Learning   A detailed understanding of users contributes to the understanding of the
Web's evolution, and to the development of Web applications. Although for new
Web platforms such a study is especially important, it is often jeopardized by
the lack of knowledge about novel phenomena due to the sparsity of data. Akin
to human transfer of experiences from one domain to the next, transfer learning
as a subfield of machine learning adapts knowledge acquired in one domain to a
new domain. We systematically investigate how the concept of transfer learning
may be applied to the study of users on newly created (emerging) Web platforms,
and propose our transfer learning-based approach, TraNet. We show two use cases
where TraNet is applied to tasks involving the identification of user trust and
roles on different Web platforms. We compare the performance of TraNet with
other approaches and find that our approach can best transfer knowledge on
users across platforms in the given tasks.

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The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
18
The categories of this paper are: econ
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th