Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
User Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6762 |
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| author | Xiaqing Xie Hangjiang Guo Yueming Lu Tianle Zhang |
| author_facet | Xiaqing Xie Hangjiang Guo Yueming Lu Tianle Zhang |
| author_sort | Xiaqing Xie |
| collection | DOAJ |
| description | User Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL method has achieved a balance between performance and interpretability, but it still faces several challenges, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. To address these challenges, this paper presents Adap-UIL, a multi-feature UIL framework based on an Adaptive Graph Walk. First, we design and implement an Adaptive Graph Walk method based on the Restarted Affinity Coefficient (RAC), which enhances both the neighborhood and higher-order features of nodes, and then we integrate cross-network features to form Adap-UIL with a more enriched node representation, facilitating user identity linkage. Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively. |
| format | Article |
| id | doaj-art-d6982ef9995e41648c4beeffbf3b1bbe |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d6982ef9995e41648c4beeffbf3b1bbe2025-08-20T03:32:28ZengMDPI AGApplied Sciences2076-34172025-06-011512676210.3390/app15126762Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph WalkXiaqing Xie0Hangjiang Guo1Yueming Lu2Tianle Zhang3Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, ChinaSchool of Cybersecurity Security, Beijing University of Posts and Communications, Beijing 100876, ChinaKey Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, ChinaKey Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, ChinaUser Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL method has achieved a balance between performance and interpretability, but it still faces several challenges, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. To address these challenges, this paper presents Adap-UIL, a multi-feature UIL framework based on an Adaptive Graph Walk. First, we design and implement an Adaptive Graph Walk method based on the Restarted Affinity Coefficient (RAC), which enhances both the neighborhood and higher-order features of nodes, and then we integrate cross-network features to form Adap-UIL with a more enriched node representation, facilitating user identity linkage. Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively.https://www.mdpi.com/2076-3417/15/12/6762restarted affinity coefficientuser alignmentuser identity linkage |
| spellingShingle | Xiaqing Xie Hangjiang Guo Yueming Lu Tianle Zhang Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk Applied Sciences restarted affinity coefficient user alignment user identity linkage |
| title | Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk |
| title_full | Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk |
| title_fullStr | Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk |
| title_full_unstemmed | Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk |
| title_short | Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk |
| title_sort | adap uil a multi feature aware user identity linkage framework based on an adaptive graph walk |
| topic | restarted affinity coefficient user alignment user identity linkage |
| url | https://www.mdpi.com/2076-3417/15/12/6762 |
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