Unsupervised Belief Representation Learning in Polarized Networks: A Variational Graph Auto-Encoder Approach. (arXiv:2110.00210v4 [cs.SI] UPDATED)

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 …

arxiv graph learning networks unsupervised