## 4 Experimental Evaluation
### Experimental Setup
The aforementioned process is separately computed at _top_\(t\) (\(t\) = 5, 10, 20, 30) _results_; finally, the average accuracy over all values of \(t\) (e.g., we calculate the MAP value by taking the average of four MAP values at top 5, 10, 20, and 30 results) is regarded as the _final_ accuracy for the dataset. We implemented the matrix forms of SimRank, JacSim, JPRank, and SimRank* by applying the default parameter settings suggested by their original work with _all_ datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(|V|\) & \(|E|\) & \#Labels & **Graph Type** & **Label Type** \\ \hline BlogCatalog & 10,312 & 333,982 & 39 & undirected & full \\ Cora & 23,166 & 91,500 & 70 & directed & full \\ DBLP & 21,177 & 248,131 & 11 & directed & partial \\ TREC & 43,202 & 347,702 & 11 & directed & partial \\ Wikipedia & 4,777 & 184,812 & 40 & undirected & full \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some statistics about our datasets. We set the impact factor \(C\) as 0.8 for all four measures. For JacSim, the importance factor \(\alpha\) is set as 0.4 by following [1]. For JPRank, both importance factors \(\alpha_{1}\) and \(\alpha_{2}\) are set as 0.4 by following [17]; however, to indicate the value of weighting parameter \(\beta\), we conducted a very simple and fast experiment by following [17] to find out between in-links and out-links which one is more beneficial to similarity computation as follows. With each dataset, we computed SimRank based on in-links and out-links only on four iterations and then compared the accuracy of these two computations; \(\beta\) is set as 0.9 if the similarity computation based on in-links shows better accuracy (i.e., with the DBLP dataset); it is set as 0.1, otherwise (i.e., with the TREC and Cora datasets (As explained in Section 2, JPRank behaves the same as JacSim with undirected graphs; we do not apply it to these graphs)). In the case of embedding methods, we utilized the implementations with the default parameter settings provided by their original work (i.e., ATP ([[https://github.com/zhenv5/aptflapt-directed-graph-embedding-with-asymmetric-transitivity-preservation](https://github.com/zhenv5/aptflapt-directed-graph-embedding-with-asymmetric-transitivity-preservation)]([https://github.com/zhenv5/aptflapt-directed-graph-embedding-with-asymmetric-transitivity-preservation](https://github.com/zhenv5/aptflapt-directed-graph-embedding-with-asymmetric-transitivity-preservation))), BoostNE ([[https://github.com/benedekrozemberczki/BoostedFactorization](https://github.com/benedekrozemberczki/BoostedFactorization)]([https://github.com/benedekrozemberczki/BoostedFactorization](https://github.com/benedekrozemberczki/BoostedFactorization))), DeepWalk ([[https://github.com/phanein/deepwalk](https://github.com/phanein/deepwalk)]([https://github.com/phanein/deepwalk](https://github.com/phanein/deepwalk))), DWNS ([[https://github.com/wonniu/AdvT4](https://github.com/wonniu/AdvT4) NE_WWW2019]([https://github.com/wonniu/AdvT4](https://github.com/wonniu/AdvT4) NE_WWW2019)), graphGAN ([[https://github.com/hwwang55/GraphGAN](https://github.com/hwwang55/GraphGAN)]([https://github.com/hwwang55/GraphGAN](https://github.com/hwwang55/GraphGAN))), Line ([[https://github.com/tangjianpku/LINE](https://github.com/tangjianpku/LINE)]([https://github.com/tangjianpku/LINE](https://github.com/tangjianpku/LINE))), NERD ([[https://git.I3s.uni-hannover.de/khosla/nerd](https://git.I3s.uni-hannover.de/khosla/nerd)]([https://git.I3s.uni-hannover.de/khosla/nerd](https://git.I3s.uni-hannover.de/khosla/nerd))), NetMF ([[https://github.com/xptree/NetMF](https://github.com/xptree/NetMF)]([https://github.com/xptree/NetMF](https://github.com/xptree/NetMF))), and node2 vec ([[https://github.com/aditya-grover/node2](https://github.com/aditya-grover/node2) vec]([https://github.com/aditya-grover/node2](https://github.com/aditya-grover/node2) vec)).