## 4 Experimental Evaluation
### Wikipedia Dataset  
Figure 10 illustrates the accuracy of all embedding methods and similarity measures with the Wikipedia dataset, and Table 12 shows the precision values for all methods. As observed in the figure, DeepWalk shows the _highest_ accuracy among embedding methods in terms of MAP, precision, recall, PRES, and F-score; however, its accuracy is close to that of DWNS, while graphGAN shows the _worst_ accuracy. Among similarity measures, JacSim shows the best accuracy, while SimRank shows the worst one in terms of all metrics. Now,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **Line** & **NERD** & **NetMF** & **node2 vec** & **JacSim** & **JPRank** & **SimRank** \\ \hline
0.018 & 0.008 & 0.033 & 0.026 & 0.022 & 0.005 & 0.034 & 0.021 & 0.019 & 0.031 & 0.035 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Precision values with the TREC dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **Eing** & **NERD** & **NetMF** & **node2 vec** & **JacSim** & **SimRank*** & **SimRank** \\ \hline MAP & 156.37 & 304.33 & \(-9.82\) & 45.81 & 15.95 & 617.85 & \(-7.38\) & 69.67 & 79.46 & \(-23.56\) & 211.07 \\ precision & 94.61 & 315.85 & 3.46 & 35.58 & 58.54 & 548.69 & 2.97 & 64.33 & 78.74 & 12.43 & 127.45 \\ recall & 97.67 & 346.31 & 5.82 & 37.06 & 81.61 & 780.16 & 6.03 & 71.36 & 64.31 & 31.32 & 91.21 \\ PRES & 110.68 & 343.59 & \(-0.37\) & 36.54 & 49.24 & 662.63 & \(-0.73\) & 64.85 & 77.88 & 6.68 & 132.30 \\ F-score & 94.42 & 331.43 & 3.74 & 34.57 & 67.83 & 651.72 & 3.52 & 66.84 & 71.41 & 20.90 & 107.64 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy improvements (%) by SimRank* over other methods with the TREC dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **graphGAN** & **Line** & **NERD** & **NetMF** & **node2 vec** & **JacSim** & **SimRank*** & **SimRank** \\ \hline MAP & 306.56 & 441.78 & 64.89 & 168.03 & 603.43 & 363.51 & 150.31 & 1042.26 & 373.62 & 1.77 & 135.37 & 168.23 \\ precision & 195.25 & 314.09 & 50.34 & 111.59 & 382.84 & 246.79 & 119.44 & 665.05 & 329.97 & 2.44 & 82.36 & 82.17 \\ recall & 183.36 & 300.94 & 51.70 & 109.85 & 382.53 & 210.52 & 119.18 & 668.47 & 375.16 & 2.46 & 60.41 & 59.66 \\ PRES & 213.05 & 343.68 & 52.71 & 120.69 & 438.64 & 262.21 & 125.86 & 705.20 & 375.48 & 2.05 & 90.03 & 94.22 \\ F-score & 188.63 & 307.11 & 51.11 & 110.32 & 382.18 & 216.87 & 118.79 & 663.16 & 353.98 & 2.47 & 69.03 & 67.67 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy improvements (%) by JPRank over other methods with the DBLP dataset.  
Figure 9: Accuracy of embedding methods and similarity measures with the TREC dataset.  
by comparing the accuracy of DeepWalk with that of JacSim, it is observed that JacSim _outperforms_ DeepWalk by 140.20%, 34.91%, 74.16%, 83.45%, and 66.55% in terms of MAP, precision, recall, PRES, and F-score; Table 13 shows the percentage of improvements in accuracy obtained by JacSim over all other methods with the Wikipedia dataset.