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Dirichlet Process Prior for Student’s t Graph Variational Autoencoders

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  • Yuexuan Zhao

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Key Laboratory of Symbolic Computation and Knowledge Engineer, Ministry of Education, Jilin University, Changchun 130012, China)

  • Jing Huang

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    Key Laboratory of Symbolic Computation and Knowledge Engineer, Ministry of Education, Jilin University, Changchun 130012, China)

Abstract

Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction. However, several pieces of research based on GVAE employ a plain prior distribution for latent variables, for instance, standard normal distribution (N(0,1)). Although this kind of simple distribution has the advantage of convenient calculation, it will also make latent variables contain relatively little helpful information. The lack of adequate expression of nodes will inevitably affect the process of generating graphs, which will eventually lead to the discovery of only external relations and the neglect of some complex internal correlations. In this paper, we present a novel prior distribution for GVAE, called Dirichlet process (DP) construction for Student’s t (St) distribution. The DP allows the latent variables to adapt their complexity during learning and then cooperates with heavy-tailed St distribution to approach sufficient node representation. Experimental results show that this method can achieve a relatively better performance against the baselines.

Suggested Citation

  • Yuexuan Zhao & Jing Huang, 2021. "Dirichlet Process Prior for Student’s t Graph Variational Autoencoders," Future Internet, MDPI, vol. 13(3), pages 1-14, March.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:75-:d:517886
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    References listed on IDEAS

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    1. Kotz,Samuel & Nadarajah,Saralees, 2004. "Multivariate T-Distributions and Their Applications," Cambridge Books, Cambridge University Press, number 9780521826549, January.
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