# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 6 Experiments and Results
### Experimental Results and Analysis  
In terms of parameter consistency, the _dropout_, _learningrate_, and _training_rounds_ parameters are the same for all models. In the network embedding part, we use Hierarchical Softmax; we set _window_size_ = 10, as the transition matrix converges at about \(k=10\) \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **V** & **E** & **Anchor Nodes** \\ \hline Douban-Online & 3906 & 8164 & 1118 \\ Douban-Offline & 1118 & 1511 & \\ \hline Facebook & 1792 & 2105 & 1515 \\ Twitter & 3493 & 6347 & \\ \hline \end{tabular}
\end{table} 
Table 1: Statistics of datasets. \(walk\_num\) = 50, \(walk\_len\) = 50 are set based on the comparison experiments among different \(walk\_num\) and \(walk\_len\). For the mapping process, the activate function is RELU and the \(learning_{rate}\) is 0.0001, and the \(batch\_size\) is 32; these parameter settings are determined based on our previous experience, aiming to strike an optimal balance between training efficiency and model convergence. Tables 2 and 3 show the results on the Douban Online-Offline dataset and Facebook-Twitter dataset; the training set ratio is 0.8, the total dimension is 256, the restart probability \(\alpha\) is 0.4, and the cross-network walk probability \(\beta\) is 0.5. As shown in Table 2, the AUC and Hit Precision indicators are almost identical for all models, so they can be ignored for this comparison. Due to the imbalance of the data, the Accuracy values of all models are less than 0.5, while Adap-UIL and PALE are closest to 0.5, which is a quite significant advantage over several other models. In contrast, the advantage of Adap-UIL in the Precision@k metrics is more obvious: p@5, p@10, p@20, p@30 are on average 0.20, 0.16, 0.11, 0.09 higher than the other models, and p@5 and p@10 are about 5 percentage points higher than the best model in the baseline. We can conclude that it is able to more accurately detect positive samples with higher confidence, or that it captures the key features better and more quickly. As \(k\) increases, p@30 also has a slight advantage over the best baseline results, indicating that it is able to maintain a better generalization performance at the same time. Figure 5 demonstrates that the validation loss (about 0.5) is substantially higher than the training loss (about 0.1), highlighting a fundamental challenge in social network user identity linkage tasks. Although the model effectively captures strong matching signals (achieving 0.95 p@30), its ability to generalize to ambiguous cases is limited due to cross-platform data heterogeneity and sparse alignment supervision. Although the early stopping strategy (\(patience=5\)) successfully prevents overfitting to source network biases, the inherent uncertainty arising from unseen network pairs in the validation set naturally leads to higher prediction variance. Table 3 shows the results on the Facebook-Twitter dataset. In terms of parameters, the training set ratio is 0.8, the dimension is 256, the restart ratio \(\alpha\) is 0.4, and the cross-network walk probability \(\beta\) is 0.5. Based on the results of the previous experiments, the values of the metrics of AUC and Hit-Precision are removed in the current experiment, as they are of low discriminative power. Table 3 shows that Adap-UIL has an average improvement of 10 percentage points over the baselines, and has advantages of several percentage points over the best baseline method. In particular, there is an increase of about 4 percentage points in p@20 and p@30. Above all, the experiments on the two datasets showed that Adap-UIL has a better performance than the baselines, especially on the metrics \(precision@k\).