Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Epitomic Variational Graph Autoencoder   Variational autoencoder (VAE) is a widely used generative model for learning
latent representations. Burda et al. in their seminal paper showed that
learning capacity of VAE is limited by over-pruning. It is a phenomenon where a
significant number of latent variables fail to capture any information about
the input data and the corresponding hidden units become inactive. This
adversely affects learning diverse and interpretable latent representations. As
variational graph autoencoder (VGAE) extends VAE for graph-structured data, it
inherits the over-pruning problem. In this paper, we adopt a model based
approach and propose epitomic VGAE (EVGAE),a generative variational framework
for graph datasets which successfully mitigates the over-pruning problem and
also boosts the generative ability of VGAE. We consider EVGAE to consist of
multiple sparse VGAE models, called epitomes, that are groups of latent
variables sharing the latent space. This approach aids in increasing active
units as epitomes compete to learn better representation of the graph data. We
verify our claims via experiments on three benchmark datasets. Our experiments
show that EVGAE has a better generative ability than VGAE. Moreover, EVGAE
outperforms VGAE on link prediction task in citation networks.

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The categories of this paper are: supr-con
2
The categories of this paper are: math
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga