Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Data Quality and Management · Complex Network Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China
School of Cybersecurity Security, Beijing University of Posts and Communications, Beijing 100876, China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China
References (37)
Show source-provided references
1. (2024, April 25). CNNIC. 2024. 53rd Statistical Report on the Development Status of China’s Internet. Available online: https://www.cnnic.net.cn/n4/2024/0322/c88-10964.html.
2. Saxena, S., and Chandra, J. (2024, January 3–9). A Survey on Network Alignment: Approaches, Applications and Future Directions. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, Jeju, Republic of Korea.10.24963/ijcai.2024/908 ↗
3. Zafarani, R., and Liu, H. (2009, January 17–20). Connecting Corresponding Identities across Communities. Proceedings of the Third International Conference on Weblogs and Social Media, San Jose, CA, USA.
4. Global Alignment of Multiple Protein Interaction Networks with Application to Functional Orthology Detection10.1073/pnas.0806627105 ↗
5. Koutra, D., Tong, H., and Lubensky, D. (2013, January 7–10). Big-align: Fast Bipartite Graph Alignment. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.10.1109/ICDM.2013.152 ↗
6. Heimann, M., Shen, H., Safavi, T., and Koutra, D. (2018, January 22–26). Regal: Representation Learning-based Graph Alignment. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Turin, Italy.10.1145/3269206.3271788 ↗
7. Zhang, S., and Tong, H. (2016, January 13–17). Final: Fast Attributed Network Alignment. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939766 ↗
8. Maretic, H.P., Gheche, M.E., Chierchia, G., and Frossard, P. (2019). Got: An Optimal Transport Framework for Graph Comparison. Advances in Neural Information Processing Systems, Curran Associates Inc.
9. Xu, H., Luo, D., and Carin, L. (2019). Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching. Advances in Neural Information Processing Systems, MIT Press.
10. Li, C., Wang, S., Yu, P.S., Zheng, L., Zhang, X., Li, Z., and Liang, Y. (2018, January 22–26). Distribution Distance Minimization for Unsupervised User Identity Linkage. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Toronto, ON, Canada.10.1145/3269206.3271675 ↗
11. Zeng, K., Dong, Z., Hou, L., Cao, Y., Hu, M., Yu, J., Lv, X., Li, J., and Feng, L. (2022, January 7–11). Interactive Contrastive Learning for Self-Supervised Entity Alignment. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Baltimore, MD, USA.10.1145/3511808.3557364 ↗
12. Zeng, Z., Du, B., Zhang, S., Xia, Y., Liu, Z., and Tong, H. (2023). Hierarchical Multi-Marginal Optimal Transport for Network Alignment. arXiv.
13. Sinkhorn Distance Minimization for Adaptive Semi-Supervised Social Network Alignment
14. Wang, Y., Zhao, Y., Wang, D.Z., and Li, L. (2023, January 10–16). Galopa: Graph Transport Learning with Optimal Plan Alignment. Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems, Virtual.
15. Network Alignment Using Graphlet Signature and High Order Proximity10.1007/978-3-030-37599-7_12 ↗
16. SAlign: A Graph Neural Attention Framework for Aligning Structurally Heterogeneous Networks10.1613/jair.1.14427 ↗
17. Trung, H.T., Vinh, T.V., Tam, N.T., Yin, H., Weidlich, M., and Hung, N.Q.V. (2020, January 20–24). Adaptive Network Alignment with Unsupervised and Multi-Order Convolutional Networks. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering, Dallas, TX, USA.10.1109/ICDE48307.2020.00015 ↗
18. Gao, J., Huang, X., and Li, J. (2021, January 15–19). Unsupervised Graph Alignment with Wasserstein Distance Discriminator. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore.10.1145/3447548.3467332 ↗
19. Sun, X., Yin, H., Liu, B., Chen, H., Meng, Q., Han, W., and Cao, J. (2021, January 19–23). Multi-Level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.10.1145/3442381.3449912 ↗
20. Zhang, S., Tong, H., Jin, L., Xia, Y., and Guo, Y. (2021, January 15–19). Balancing Consistency and Disparity in Network Alignment. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore.10.1145/3447548.3467331 ↗
21. Wang, F., Sun, L., and Zhang, Z. (2020, January 7–11). Hyperbolic user identity linkage across social networks. Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference, Taipei, Taiwan.10.1109/GLOBECOM42002.2020.9322242 ↗
22. Sun, L., Zhang, Z., Zhang, J., Wang, F., Du, Y., Su, S., and Yu, P.S. (2020, January 14–17). PERFECT: A Hyperbolic Embedding for Joint User and Community Alignment. Proceedings of the 2020 IEEE International Conference on Data Mining, Sorrento, Italy.10.1109/ICDM50108.2020.00059 ↗
23. Zhou, F., Li, C., Xu, X., Liu, L., and Trajcevski, G. (2021, January 6–10). HGENA: A Hyperbolic Graph Embedding Approach for Network Alignment. Proceedings of the 2021 IEEE Global Communications Conference, Madrid, Spain.10.1109/GLOBECOM46510.2021.9685690 ↗
24. HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment10.1016/j.ipm.2022.103021 ↗
25. Geometry Interaction Network Alignment10.1016/j.neucom.2022.06.077 ↗
26. Man, T., Shen, H., Liu, S., Jin, X., and Cheng, X. (2016, January 7–15). Predict Anchor Links Across Social Networks via an Embedding Approach. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA.
27. Zhou, F., Liu, L., Zhang, K., Trajcevski, G., Wu, J., and Zhong, T. (2018, January 16–19). DeepLink: A Deep Learning Approach for User Identity Linkage. Proceedings of the IEEE INFOCOM 2018—IEEE Conference on Computer Communications, Honolulu, HI, USA.10.1109/INFOCOM.2018.8486231 ↗
28. Structure Based User Identification Across Social Networks10.1109/TKDE.2017.2784430 ↗
29. Du, X., Yan, J., and Zha, H. (2019, January 10–16). Joint Link Prediction and Network Alignment via Cross-Graph Embedding. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.10.24963/ijcai.2019/312 ↗
30. Cross-Graph Embedding with Trainable Proximity for Graph Alignment10.1109/TKDE.2023.3270119 ↗
31. Yan, Y., Zhang, S., and Tong, H. (2021, January 19–23). BRIGHT: A Bridging Algorithm for Network Alignment. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.10.1145/3442381.3450053 ↗
32. Xiong, Z., Xie, X., Wu, X., Peng, Y., and Lu, Y. (2023, January 3–5). DSANE: A Dual Structure-Aware Network Embedding Approach for User Identity Linkage. Proceedings of the 2023 IEEE 8th International Conference on Big Data Analytics, Hong Kong, China.10.1109/ICBDA57405.2023.10104627 ↗
33. Zhen, Y., Hu, R., Li, D., and Xiao, Y. (2022, January 18–23). User Alignment Across Social Networks Based on Ego-Network Embedding. Proceedings of the 2022 International Joint Conference on Neural Networks, Padua, Italy.10.1109/IJCNN55064.2022.9892481 ↗
34. Xie, X., Zang, W., Hu, Y., Ji, J., and Xiong, Z. (2024). Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage. Electronics, 13.10.3390/electronics13040715 ↗
35. Network embedding: Taxonomies, frameworks and applications
36. Zhang, Y., Tang, J., Yang, Z., Pei, J., and Yu, P.S. (2015, January 10–13). COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia.10.1145/2783258.2783268 ↗
37. Liu, L., Cheung, W.K., Li, X., and Yin, Z. (2016, January 9–15). Aligning Users across Social Networks Using Network Embedding. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York, NY, USA.