# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 5 Experiments
### Experimental Results  
**Community Detection. ** The results are exhibited in Figure 2. We can find that the proposed SAEM+c2n2v outperforms the other competing methods on three privacy protection cases across three datasets. Although SAEM by itself cannot work well, it is able to significantly boost the performance of n2v. In addition, when users are simulated to protect more privacy, i.e., revealing less information in the x-axis, the superiority of our SAEM-based NRL models tends to be more significant, as comparing to n2v. Such results imply that SAEM is capable of effectively combining graph structure and user \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **\#Nodes** & **\#Edges** & **\#Components** & **\#Attributes** \\ \hline Cora & 2708 & 5429 & 78 & 1433 \\ Citeseer & 3312 & 4715 & 438 & 3703 \\ Twitter & 212 & 5054 & 1.6 & 1178 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of three network datasets. attributes to alleviate the issue of data incompleteness, and eventually generating high-quality node embeddings. Moreover, it is interesting to see that the superiority of SAEM+cn2 nv on Twitter data are not so significant as the other datasets. We think the possible reason is that Twitter data is not so sparse, i.e., the number of disconnected components is quite small, as presented in Table 1.  
**Link Prediction. ** We show the results in Figure 3. It can be found that the tendency of LP task is quite similar to that of the CD task. SAEM+c2n2v generates the highest AUC scores in all cases. There is one additional finding. The superiority gap between our SAEM-based NRL models and other methods is larger than those in CD task on Cora and Citeseer, and the superiority is smaller on Twitter. Here is the potential reason. Link prediction needs to generate features of node pairs. Since disconnected components lead to worse-quality node embeddings, features of node pairs based on Hadamard operator are accordingly ineffective in depicting the correlatedness between nodes. These results deliver an insight: SAEM particularly works on social networks with more disconnected components, and it is especially useful for friendship recommendation. Figure 2: Results of Normalized Mutual Information (NMI) scores on community detection across three privacy settings and three datasets. **Node Classification. ** The results are exhibited in Figure 4. Our SAEM-based NRL models again outperform the other competing methods. SAEM itself can be seen as an effective feature representation, especially when users protect more privacy (i.e., lower \"reserved (%)\"). Nevertheless, the superiority gaps are not so significant as those on CD and LP tasks. The classifier is able to some extent distinguish different labels, so the negative effect of disconnected components is not strong. **n2v vs. c2n2v. ** To further understand the effectiveness of the proposed c2n2v, we provide a detailed comparison between n2v and c2n2v with SAEM, as shown in Table 2. The results show that c2n2v can moderately improve the performance across datasets, tasks, and privacy settings. **t-SNE Visualization and Explainability. ** The model explainability and interpretability is an essential issue in deep learning and artificial intelligence models [30; 31; 32; 33]. People usually expect that a black-box model can deliver some intuitive concept while producing satisfying performance. Here, we would like to present the model explainability of the proposed privacy-aware NRL via visualization. We exhibit the results of t-SNE visualization for the typical NRL model node2 vec (n2v) and our SAEM+c2n2v in Figure 5. The t-SNE visualization can be also considering as explaining the effect of the proposed SAEM method. The interpretability comes from how nodes with different color labels distribute in the t-SNE embedding space. It can be apparently found that our model is able to effectively group nodes with the same labels (colors) together.