# Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
## 4 Experiments
### Main Results
#### 4.3.3 Graph Visualization  
Visualization is another intuitive and effective way to assess the performance of network embedding methods. We get node representations through SAVGAE model, then map these points into 2D space by t-SNE [30]. The color denotes the class label of the nodes in the dataset, which is not exploited in our unsupervised attributed network embedding. Therefore, nodes in the same color should locate closer to each other. As in Figure 2, the meaningful clustering layout demonstrates the performance of SAVGAE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cora** & **Acc** & **NMI** & **F1** & **Precision** & **ARI** \\ \hline K-means & 0.492 & 0.321 & 0.368 & 0.369 & 0.230 \\ SC & 0.367 & 0.127 & 0.318 & 0.193 & 0.031 \\ GraphEncoder & 0.325 & 0.109 & 0.298 & 0.182 & 0.006 \\ DeepWalk & 0.484 & 0.327 & 0.392 & 0.361 & 0.243 \\ node2 vec & 0.526 & 0.343 & 0.413 & 0.393 & 0.284 \\ DNGR & 0.419 & 0.318 & 0.340 & 0.266 & 0.142 \\ RTM & 0.440 & 0.230 & 0.307 & 0.332 & 0.169 \\ RMSC & 0.407 & 0.255 & 0.331 & 0.227 & 0.090 \\ TADW & 0.560 & 0.441 & 0.481 & 0.396 & 0.332 \\ GAE & 0.596 & 0.429 & 0.595 & 0.596 & 0.347 \\ VGAE & 0.609 & 0.436 & 0.609 & 0.609 & 0.346 \\ ARGE & 0.640 & 0.449 & 0.619 & 0.646 & 0.352 \\ ARVGE & 0.638 & 0.450 & 0.627 & 0.624 & 0.374 \\ SAVGAE(ours) & **0.690** & **0.497** & **0.695** & **0.701** & **0.429** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering Results on Cora. Bold font indicates the best result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cora** & **Acc** & **NMI** & **F1** & **Precision** & **ARI** \\ \hline K-means & 0.540 & 0.305 & 0.409 & 0.405 & 0.279 \\ SC & 0.239 & 0.056 & 0.299 & 0.179 & 0.010 \\ GraphEncoder & 0.225 & 0.033 & 0.301 & 0.179 & 0.010 \\ DeepWalk & 0.337 & 0.088 & 0.270 & 0.248 & 0.092 \\ node2 vec & 0.374 & 0.105 & 0.291 & 0.276 & 0.125 \\ DNGR & 0.326 & 0.180 & 0.300 & 0.200 & 0.044 \\ RTM & 0.451 & 0.239 & 0.342 & 0.349 & 0.203 \\ RMSC & 0.295 & 0.139 & 0.320 & 0.204 & 0.049 \\ TADW & 0.455 & 0.291 & 0.414 & 0.312 & 0.228 \\ GAE & 0.408 & 0.176 & 0.372 & 0.418 & 0.124 \\ VGAE & 0.344 & 0.156 & 0.308 & 0.349 & 0.093 \\ ARGE & 0.573 & 0.350 & 0.546 & 0.573 & 0.341 \\ ARVGE & 0.544 & 0.261 & 0.529 & 0.549 & 0.245 \\ SAVGAE(ours) & **0.582** & **0.352** & **0.573** & **0.596** & **0.364** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Clustering Results on Citeseer. Bold font indicates the best result.  
generative adversarial networks. We use Rooted PageRank (RPR) rather than adjacency matrix as the reconstruction objective of the auto-encoder to preserve local and global proximity simultaneously in node representation while not increasing much model complexity. The sampling process is an indispensable part of VAE before getting a latent representation of the node, but it causes the problem that the node embeddings are not exclusive which may affect the downstream network analysis task. We modify the VAE structure and integrate with generative adversarial networks to learn an exclusive and robust embedding for each node to overcome the aforementioned problem. Empirically, we evaluate the generated network representations in a variety of network datasets and applications. The results demonstrate the superiority of our method compared with the state-of-the-art. Our future work will focus on proposing a generalized framework that can handle unseen nodes and networks rather than just learning embeddings on a single fixed network.  
Conceptualization, J.Z. and F.L.; methodology, J.Z., Y.W., D.L.; validation, J.Z.; formal analysis, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, F.L., Y.W., D.L.; supervision, G.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Strategic Priority Research Program of Chinese Academy of Sciences (grant number Y835120378).