Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Assessing Network Representations for Identifying Interdisciplinarity   Many studies have sought to identify interdisciplinary research as a function
of the diversity of disciplines identified in an article's references or
citations. However, given the constant evolution of the scientific landscape,
disciplinary boundaries are shifting and blurring, making it increasingly
difficult to describe research within a strict taxonomy. In this work, we
explore the potential for graph learning methods to learn embedded
representations for research papers that encode their 'interdisciplinarity' in
a citation network. This facilitates the identification of interdisciplinary
research without the use of disciplinary categories. We evaluate these
representations and their ability to identify interdisciplinary research,
according to their utility in interdisciplinary citation prediction. We find
that those representations which preserve structural equivalence in the
citation graph are best able to predict distant, interdisciplinary interactions
in the network, according to multiple definitions of citation distance.

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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: eess