Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: R-VGAE: Relational-variational Graph Autoencoder for Unsupervised
  Prerequisite Chain Learning   The task of concept prerequisite chain learning is to automatically determine
the existence of prerequisite relationships among concept pairs. In this paper,
we frame learning prerequisite relationships among concepts as an unsupervised
task with no access to labeled concept pairs during training. We propose a
model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict
concept relations within a graph consisting of concept and resource nodes.
Results show that our unsupervised approach outperforms graph-based
semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in
terms of prerequisite relation prediction accuracy and F1 score. Our method is
notably the first graph-based model that attempts to make use of deep learning
representations for the task of unsupervised prerequisite learning. We also
expand an existing corpus which totals 1,717 English Natural Language
Processing (NLP)-related lecture slide files and manual concept pair
annotations over 322 topics.

12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
18
The categories of this paper are: econ
16
The categories of this paper are: stat