Abstract
User Identity Linkage (UIL) across social networks can be used to identify the accounts belonging to the same individual in multiple social networks, which is of great significance for user behavior analysis and network security supervision. By integrating deep learning into the process of UIL, the original intrinsic features of users can be retained more completely. However, the data distribution among different networks is different, and the existing UIL methods often ignore these differences, or only adjust a single step by domain adaptation mechanism, so it is difficult to ensure the accuracy of UIL. In this paper, we propose a new UIL model (called DualLink) which uses a dual domain adaptation mechanism to solve the problem of inconsistent data distribution. On one hand, a node embedding method based on adversarial domain adaptation is proposed to learn the node representation by considering the attributes, the topological structure and the difference between domains. On the other hand, a node matching method based on back-propagation domain adaptation is proposed to learn the suitable matching function by using back propagation neural network (BPNN). The feasibility and effectiveness of the key technology proposed in this paper are verified by experiments.
This research is supported by National Natural Science Foundation of China (62072084, 62072086), Fundamental Research Funds for the Central Universities (N2116008, N180716010).
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Xu, B., Kou, Y., Wang, G., Shen, D., Nie, T. (2021). DualLink: Dual Domain Adaptation for User Identity Linkage Across Social Networks. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_2
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