# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis
### Semi-Supervised Node Classification
#### 5.4.2 Experimental Results and Analysis  
The classification results on the Cora, Citeseer, and Pubmed datasets are shown in Tables 4, 5, 6, respectively. When the number of labels applied for training is relatively small (e.g., each class can only obtain 5 or 10 labels), the effect of promotion is particularly obvious. In comparison with the baselines, NPIVGAE and its variant has achieved excellent results on all datasets. The prediction accuracy of NPIVGAE on the Cora dataset has reached more than 81%, as depicted in Table 4. The indicators of NPIVGAE on the Citeseer dataset also exceed 69%, while the results of other baselines are relatively poor, as displayed in Table 5. It demonstrates that the NPIVGAE we proposed can learn the information in the graph so effectively that it achieves better performance.  
We selected two methods that did not use neural networks (i.e., LINE (Large-scale Information Network Embedding) [30] and TADW (text-associated DeepWalk) [31]) for comparison. LINE embeds a large-scale information network into a low-dimensional vector space while retaining the first-order and second-order similarities. TADW is a matrix factorization method that combines network structure and text features to obtain a better network representation. We conducted experiments on the semi-supervised node classification task with the Cora and Citeseer datasets as examples. To be fair, both TADW and our model NPIVGAE were iterated 50 times. We report the running time in Figure 2. From Figure 2, we know that NPIVGAE and TADW are significantly faster than LINE, and NPIVGAE is slightly faster than TADW. In addition, our model was compared with LINE and TADW on time complexity. LINE and TADW have relatively high time complexity, as shown in Table 7. TADW requires expensive matrix operations, such as SVD decomposition, which limits the ability of TADW to process large-scale data. In contrast, our model NPIVGAE has higher efficiency while guaranteeing accuracy.  
Figure 3 shows the learning process of our model in the link prediction task. Our model was iterated 200 times, and the AUC and AP of the training and validation sets are displayed in Figure 3. The horizontal axis is the number of iterations, and the vertical axis is the accuracy of AUC and AP. We can see that the training and validation accuracy of the model rose sharply in the first 20 iterations, and then gradually slowed down and converged. As mentioned in Section 5.3.1, we set the weight decay parameter to 0.01 in the experiment since the Cora and Citeseer datasets are relatively small. NPIVGAE performed well on training, validation, and test sets, proving that the strategy we adopted successfully prevented overfitting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LINE** & **TADW** & **NPIVGAE** \\ \hline Time complexity & \(O(dK|C|)\) & \(O(n^{2}+NZ(M)d+nd^{2})\) & \(O(Edn)\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The time complexity of LINE, TADW, and NPIVGAE. \(K\) is the number of negative samples, \(|C|\) is the number of edges. \(NZ(\cdot)\) indicates the number of non-zero items and \(\mathbf{M}\) represents a certain relationship matrix.  
Figure 2: Running time (in seconds) on LINE, TADW, and NPIVGAE.