Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Belief Representation Learning in Polarized Networks: A
  Variational Graph Auto-Encoder Approach   In this paper, we propose an Information-Theoretic Variational Graph
Auto-Encoder (InfoVGAE) for polarity representation learning in an unsupervised
manner. It jointly learns the belief embedding of both users and their claims
in the same latent space. In order to better disentangle the latent space, a
total correlation regularizer, a PI controller, and the rectified Gaussian
Distribution are adopted to constrain the generated distribution. Experimental
results show that the proposed InfoVGAE outperforms the existing unsupervised
polarity detection methods, and achieves a highly comparable result with
supervised methods in terms of F1 score and purity.

23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga