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\begin{document}

\title{GAN-based Deep Neural Networks for Graph Representation Learning}



\author[1]{Ming Zhao}%
\author[1]{yinglong zhang}%
\affil[1]{Minnan Normal University}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\begin{abstract}
Graph representation learning has attracted increasing attention in a
variety of applications that involve learning on non-Euclidean data.
Recently, generative adversarial networks(GAN) have been increasingly
applied to the field of graph representation learning, and large
progress has been made. However, most GAN-based graph representation
learning methods use adversarial learning strategies directly on the
update of the vector representation instead of the embedding mechanism,
which does not make full use of the essential advantages of GAN. The
essential advantage of GAN is the final embedding mechanism rather than
the embedding representation itself. To address this problem, we propose
to use adversarial idea on the reconstruction mechanism of deep
autoencoders. Specifically, the generator and the discriminator are the
two basic components of the GAN structure. We use the deep autoencoder
as the discriminator, which can capture the highly non-linear structure
of the graph. In addition, the generator another generative model is
introduced into the adversarial learning system as a competitor. A
series of empirical results proved the effectiveness of the new
approach.%
\end{abstract}%



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\textbf{Hosted file}

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