Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representation Learning using Graph Autoencoders with Residual
  Connections   Graph autoencoders are very efficient at embedding graph-based complex data
sets. However, most of the autoencoders have shallow depths and their
efficiency tends to decrease with the increase of layer depth. In this paper,
we study the effect of adding residual connections to shallow and deep graph
variational and vanilla autoencoders. We show that residual connections improve
the accuracy of the deep graph-based autoencoders. Furthermore, we propose
Res-VGAE, a graph variational autoencoder with different residual connections.
Our experiments show that our model achieves superior results when compared
with other autoencoder-based models for the link prediction task.

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The categories of this paper are: eess
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The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph