# Hypernetwork Representation Learning with the Set Constraint
## 6 Experiments
### Link Prediction  
Link prediction is widely used in real life, especially in recommendation systems. In this subsection, we carry out link prediction tasks on all four datasets. As for the link prediction, the evaluation measure AUC [30] is adopted. As shown in Tables 5-8, the edges \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Training Ratios**} \\ \cline{2-10}  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline Deepwalk & 48.01 & 50.35 & 51.41 & 52.60 & 52.59 & 53.47 & 53.57 & 54.23 & 54.09 \\ node2 vec & 46.93 & 49.28 & 50.77 & 51.51 & 52.62 & 52.58 & 53.04 & 53.44 & 52.71 \\ LINE & 43.93 & 45.46 & 46.52 & 47.29 & 47.70 & 48.16 & 48.02 & 49.09 & 48.34 \\ GraRep & 47.75 & 50.11 & 51.16 & 52.01 & 52.10 & 53.15 & 53.34 & 53.43 & 53.24 \\ HOPE & 46.33 & 48.57 & 49.95 & 50.69 & 51.06 & 51.04 & 51.29 & 52.53 & 51.79 \\ SDNE & 41.74 & 41.79 & 42.34 & 42.73 & 43.36 & 43.27 & 43.89 & 43.43 & 42.81 \\ HRSC & 48.60 & 50.81 & 52.02 & 53.19 & 53.73 & 54.12 & 54.83 & 54.95 & 56.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Node classification accuracies on MovieLens (%). with different ratios are randomly removed from the four datasets respectively to evaluate the AUC. From Tables 5-8, we have following observations:  
* On the GPS, drug and wordnet datasets, the link prediction performance of HRSC is superior to that of other baseline methods. On the MovieLens dataset, the link prediction performance of HRSC is better than that of most of other baseline methods, specially, HRSC performs better than DeepWalk and GraRep at the smaller training \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Training Ratios**} \\ \cline{2-9}  & **60\%** & **65\%** & **70\%** & **75\%** & **80\%** & **85\%** & **90\%** \\ \hline Deepwalk & 0.7845 & 0.8129 & 0.8301 & 0.8440 & 0.8729 & 0.8800 & 0.9025 \\ node2 vec & 0.7078 & 0.7390 & 0.7418 & 0.7696 & 0.7939 & 0.8036 & 0.8296 \\ LINE & 0.8282 & 0.8242 & 0.8253 & 0.8320 & 0.8365 & 0.8172 & 0.8231 \\ GraRep & 0.7290 & 0.7833 & 0.7907 & 0.8121 & 0.8277 & 0.8481 & 0.8544 \\ HOPE & 0.6895 & 0.7333 & 0.7203 & 0.7522 & 0.7787 & 0.7986 & 0.8049 \\ SDNE & 0.4004 & 0.3511 & 0.3494 & 0.3406 & 0.3433 & 0.3598 & 0.4171 \\ HRSC & 0.8714 & 0.8706 & 0.8681 & 0.8644 & 0.8706 & 0.8651 & 0.8528 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AUC values on MovieLens. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Training Ratios**} \\ \cline{2-9}  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline Deepwalk & 29.91 & 33.44 & 34.53 & 35.05 & 35.70 & 36.80 & 37.93 & 36.71 & 39.00 \\ node2 vec & 29.27 & 32.23 & 33.71 & 34.52 & 36.17 & 36.05 & 37.53 & 37.66 & 37.30 \\ LINE & 22.77 & 24.11 & 25.11 & 24.94 & 25.23 & 25.59 & 25.87 & 26.60 & 25.44 \\ GraRep & 32.59 & 34.74 & 34.63 & 35.21 & 35.38 & 36.05 & 35.10 & 36.63 & 37.79 \\ HOPE & 30.53 & 33.61 & 35.02 & 35.97 & 34.90 & 35.11 & 36.21 & 36.20 & 34.84 \\ SDNE & 21.96 & 21.57 & 22.05 & 22.37 & 23.26 & 22.59 & 23.63 & 23.60 & 25.31 \\ HRSC & 31.54 & 33.94 & 34.98 & 36.84 & 37.35 & 38.02 & 38.78 & 40.22 & 41.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Node classification accuracies on wordnet (%).