# Dirichlet Process Prior for Student's t Graph Variational Autoencoders
## 6 Conclusions  
In this work, we propose a DP construction for Student's t distribution prior, and combine it with GVAE. We solve the inaccuracy problem of modeling real-world networks by manually setting latent dimensions through the adaptive complexity of Bayesian nonparametric methods. In addition, the advantages and disadvantages of applying Gaussian distribution in traditional VAEs are analyzed at length, and we have illustrated that the application of Student's t distribution can effectively improve the limitation of effective latent features. Here, our DP-St GVAE is used to the network structure data, and the combination of the infinite-dimensional nonparametric generation process and heavy-tailed distribution plays an important role in network presentation learning tasks. In the future, our work will proceed, and there are two primary directions: (i) applying the DP-St to deep neural networks so that the problem of future research will break the limitation of graph structure data; (ii) using other Bayesian nonparametric methods for further improving the prediction accuracy of network representation learning tasks. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **5 Labels** & **10 Labels** & **20 Labels** \\ \hline GAT & 56.7 \(\pm\) 5.1 & 64.1 \(\pm\) 3.3 & 67.6 \(\pm\) 2.3 \\ GCN & 58.5 \(\pm\) 4.7 & 65.4 \(\pm\) 2.6 & 67.8 \(\pm\) 2.3 \\ GVAE & 49.1 \(\pm\) 1.8 & 52.1 \(\pm\) 1.4 & 52.4 \(\pm\) 2.5 \\ St GVAE & 52.5 \(\pm\) 1.3 & 53.1 \(\pm\) 2.9 & 53.8 \(\pm\) 1.5 \\ DP-St GVAE & 59.2 \(\pm\) 2.5 & 64.5 \(\pm\) 2.5 & 67.9 \(\pm\) 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Semi-supervised node classification prediction accuracy for Citeseer. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **5 Labels** & **10 Labels** & **20 Labels** \\ \hline GAT & 68.0 \(\pm\) 4.8 & 72.6 \(\pm\) 3.6 & 76.4 \(\pm\) 3.0 \\ GCN & 69.7 \(\pm\) 4.5 & 73.9 \(\pm\) 3.4 & 77.5 \(\pm\) 2.5 \\ GVAE & 74.6 \(\pm\) 1.9 & 74.9 \(\pm\) 2.3 & 75.4 \(\pm\) 3.0 \\ St GVAE & 76.2 \(\pm\) 2.6 & 77.5 \(\pm\) 3.3 & 79.0 \(\pm\) 3.9 \\ DP-St GVAE & 76.4 \(\pm\) 3.9 & 78.4 \(\pm\) 1.9 & 79.8 \(\pm\) 1.2 \\ \hline \hline \end{tabular}
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Table 7: Semi-supervised node classification prediction accuracy for PubMed. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **5 Labels** & **10 Labels** & **20 Labels** \\ \hline GAT & 70.4 \(\pm\) 3.7 & 76.6 \(\pm\) 2.8 & 79.9 \(\pm\) 1.8 \\ GCN & 70.0 \(\pm\) 3.7 & 76.0 \(\pm\) 2.2 & 79.8 \(\pm\) 1.8 \\ GVAE & 68.4 \(\pm\) 2.4 & 70.3 \(\pm\) 2.7 & 71.6 \(\pm\) 1.4 \\ St GVAE & 69.8 \(\pm\) 2.4 & 71.0 \(\pm\) 1.5 & 72.2 \(\pm\) 2.0 \\ DP-St GVAE & 75.1 \(\pm\) 2.6 & 76.9 \(\pm\) 2.7 & 79.3 \(\pm\) 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Semi-supervised node classification prediction accuracy for Cora. **Author Contributions:** Conceptualization, Y.Z. ; methodology, Y.Z. ; software, Y.Z. ; validation, Y.Z. and J.H. ; formal analysis, Y.Z. and J.H. ; investigation, Y.Z. and J.H. ; resources, Y.Z. and J.H. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. and J.H. ; visualization, Y.Z. ; supervision, J.H. ; project administration, J.H. ; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China under grant number 61572226, 61876069; Jilin Province Key Scientific and Technological Research and Development project under grant numbers 20180201067 GX, 201802010446X; Jilin Province Natural Science Foundation under grant No. 20200201036 JC. **Data Availability Statement:** Data is contained within the article. The data presented in this study are available in [Revisiting Semi-Supervised Learning with Graph Embeddings. [PERSON], [PERSON], [PERSON]. ICML 2016.