# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 6 Experiments and Results
### Ablation Experiments
#### 6.5.3 Performance Validation of Cross-Network Walk  
In order to verify the importance of the cross-network walk in the model, we consider three different walk approaches while the other parts of the model remain unchanged. The comparison experiments are as follows:  
* AM-UIL: a UIL model based on the Affinity Coefficient--the affinity coefficient is used as the probability of sequence generation, and the restart ratio \(\alpha\) is 0.
* RWR-UIL: a UIL model based on RWR. It does not use the affinity coefficient but instead uses the RWR for sequence generation. The \(\alpha=0.4\), the \(train\_ratio=0.8\), and the \(total\_dimension=256\).
* ReAM-UIL: a UIL model based on RAC; the difference with Adap-UIL is that its \(\beta\) is 0. The \(\alpha=0.4\), the \(train\_ratio=0.8\), and the \(total\_dimension=256\).
* Adap-UIL: a UIL model based on the Adaptive Graph Walk. The \(restartratio=0.4\), the \(train\_ratio=0.8\), the \(total\_dimension=256\), and the \(\beta\) is 0.5.  
From Table 6, it can be concluded that the cross-network walk has a certain effect on all the indicators. Compared with the optimal situation of the comparison model, the increase  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\mathbf{\alpha}\) & **0.0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** \\ \hline Total Score & 7 & 10 & 12 & 16 & 20 & 8 & 12 & 13 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison among different alpha \(\alpha\).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{8}{c}{**Cross-Network Walk Ratio**} \\ \cline{2-10}  & \multicolumn{2}{c}{**0.1**} & \multicolumn{2}{c}{**0.2**} & \multicolumn{2}{c}{**0.3**} & \multicolumn{2}{c}{**0.4**} & \multicolumn{2}{c}{**0.5**} \\ \cline{2-10}  & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** \\ \hline Accuracy & \(-1.0\) & \(-\) & \(1.0\) & \(-\) & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-\) & \(1.0\) \\ MAP & \(-\) & \(-\) & \(1.0\) & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-\) & \(-\) & \(1.0\) \\ AUC & \(-\) & \(-\) & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-1.0\) & \(-\) & \(1.0\) & \(1.0\) \\ Hit-precision & \(-\) & \(-\) & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-1.0\) & \(-\) & \(1.0\) & \(1.0\) \\ Precision@5 & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-\) & \(-1.0\) & \(1.0\) & \(-\) & \(-1.0\) & \(1.0\) \\ Precision@10 & \(-1.0\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-1.0\) & \(1.0\) & \(-\) & \(-\) & \(1.0\) \\ Precision@15 & \(-1.0\) & \(-1.0\) & \(-\) & \(1.0\) & \(-\) & \(-\) & \(-1.0\) & \(1.0\) & \(-\) & \(-1.0\) \\ Precision@20 & \(-\) & \(-1.0\) & \(-\) & \(-\) & \(-1.0\) & \(1.0\) & \(1.0\) & \(-\) & \(-\) & \(-\) \\ Precision@25 & \(-1.0\) & \(-\) & \(-1.0\) & \(-\) & \(-1.0\) & \(-\) & \(1.0\) & \(1.0\) & \(-\) & \(-\) & \(-\) \\ Precision@30 & \(-1.0\) & \(1.0\) & \(-\) & \(-1.0\) & \(1.0\) & \(-\) & \(-\) & \(-\) & \(-\) & \(1.0\) \\ \hline Total & \multicolumn{2}{c}{\(-7\)} & \multicolumn{2}{c}{\(-2\)} & \multicolumn{2}{c}{\(-3\)} & \multicolumn{2}{c}{\(1\)} & \multicolumn{2}{c}{8} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison for the cross-network-walk ratio.  
in Accuracy and MAP is the biggest at about 4 percent; compared with the average situation of the comparison model, the increase in \(p@5\) and\(p@10\) is the biggest at about 5 percent. Thus, the cross-network walk does improve the overall effectiveness of the model.