# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 7 Conclusions and Discussion  
This paper presents Adap-UIL, an adaptive graph walk-based framework for multi-feature-aware user identity linkage. The key innovation lies in the development of an RAC-based adaptive graph walk method that simultaneously captures both local neighborhood characteristics and higher-order structural features of nodes. By effectively integrating cross-network features, we construct more comprehensive node representations that significantly enhance the performance of user identity linkage tasks. Extensive experiments conducted on real-world datasets demonstrate that Adap-UIL consistently outperforms existing baseline methods across multiple evaluation metrics.  
However, several limitations warrant further investigation. First, we observe notable performance variations across different datasets, with particularly suboptimal results on the Facebook-Twitter dataset, indicating room for improvement in cross-platform generalization capability. Second, while the model demonstrates overall strong performance, the accuracy metrics across both datasets suggest potential for further optimization. Future research directions will focus on the following: (1) enhancing feature extraction algorithms to improve model generalization, (2) developing more effective negative sampling strategies to boost accuracy, and (3) investigating domain adaptation techniques to address performance discrepancies across different social platforms. These improvements will further strengthen the robustness and applicability of our framework in real-world scenarios.  
Conceptualization, X.X. and Y.L.; methodology, X.X. and H.G.; software, H.G.; validation, X.X. and H.G.; formal analysis, T.Z. and H.G.; investigation, H.G.; resources, Y.L.; data curation, X.X.; writing--original draft preparation, X.X.; writing--review and editing, Y.L. and T.Z.; visualization, X.X.; supervision, Y.L.; project administration, Y.L. and T.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (NO.62272117) and the National Key Research and Development Program of China (NO. 2024 YFB31 NL00102).  
Not applicable.  
The code and data are available at [[https://github.com/Xiexiaqing/Adap-UIL](https://github.com/Xiexiaqing/Adap-UIL)]([https://github.com/Xiexiaqing/Adap-UIL](https://github.com/Xiexiaqing/Adap-UIL)) (accessed on 25 April 2025).  
The authors declare no conflicts of interest. The funder played a role in the formal analysis, writing--review and editing, and in project administration.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **Accuracy** & **MAP** & **AUC** & **Hit-Precision** & **p@5** & **p@10** & **p@15** & **p@20** & **p@25** & **p@30** \\ \hline AM-UIL & 0.4018 & 0.3326 & 0.9780 & 0.9780 & 0.6696 & 0.8031 & 0.8482 & 0.8786 & 0.8964 & 0.9107 \\ RW-UIL & 0.4152 & 0.3872 & 0.9774 & 0.9774 & 0.7040 & 0.8361 & 0.8839 & 0.9063 & 0.9223 & 0.9330 \\ RWR-UIL & 0.4286 & 0.3730 & 0.9756 & 0.9756 & 0.6947 & 0.8031 & 0.8527 & 0.8799 & 0.8978 & 0.9098 \\ ReAM-UIL & 0.4375 & 0.3738 & 0.9866 & 0.9866 & 0.6705 & 0.8000 & 0.8442 & 0.8754 & 0.8929 & 0.9047 \\ Adap-UIL & 0.4598 & 0.4385 & 0.9928 & 0.9928 & 0.7411 & 0.8661 & 0.9018 & 0.9286 & 0.9420 & 0.9554 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results on Douban Online-Offline