Dirichlet Process Prior for Student’s t Graph Variational Autoencoders

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 instanc...

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Vydané v:	Future internet Ročník 13; číslo 3; s. 75
Hlavní autori:	Zhao, Yuexuan, Huang, Jing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.03.2021
Predmet:	Airports Bayesian nonparametric Coders Complexity Dirichlet problem Dirichlet process Flexibility Graph variational auto-encoder Internet Keywords network representation learning neural network Neural networks Nonparametric statistics Normal distribution Probability distribution Student’s t distribution Variables
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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.
AbstractList	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.
Author	Huang, Jing Zhao, Yuexuan
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SubjectTerms	Airports Bayesian nonparametric Coders Complexity Dirichlet problem Dirichlet process Flexibility Graph variational auto-encoder Internet Keywords network representation learning neural network Neural networks Nonparametric statistics Normal distribution Probability distribution Student’s t distribution Variables
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Title	Dirichlet Process Prior for Student’s t Graph Variational Autoencoders
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