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MMUIL: enhancing multi-platform user identity linkage with multi-information

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Abstract

User identity linkage (UIL) aims to link identities belonging to the same individual across various platforms. While numerous methods have been proposed for paired or multiple platforms, UIL is still a non-trivial task due to the following challenges. (1) How to alleviate the sparsity and incompleteness of user information from different platforms? (2) How can UIL approaches achieve high effectiveness while still maintaining low complexity in multi-platform scenarios? In light of these challenges, we propose MMUIL (enhancing multi-platform user identity linkage with multi-information), a novel model excelling in high effectiveness while still maintaining low complexity. The model consists of a Multi-Information Embedding (MIE) module and a Partially Shared Adversarial Learning (PSAL) module. Specifically, for the first challenge, MIE simultaneously considers the token sequence semantics in usernames and the structural information of multi-type networks (i.e., homogeneous and heterogeneous networks). To address the second challenge, the adversarial learning-based PSAL decreases the complexity with shared partial parameters (i.e., shared generators). Meanwhile, to enhance the model’s effectiveness, PSAL exploits an attention mechanism to mitigate the disadvantages of shared partial parameters, such as partial information loss and noise introduction, while integrating the above multi-information intensively. The extensive experiments conducted on two real-world datasets demonstrate that our proposed model MMUIL significantly outperforms the state-of-the-art methods.

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Notes

  1. https://github.com/wxqzhou/MMUIL.

  2. http://dblp.uni-trier.de.

  3. http://dblp.uni-trier.de/xml/.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China No. 62272332 and the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China No. 22KJA520006.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Shizi Street, Suzhou, 215006, Jiangsu, China

    Qian Zhou, Yihan Hei, Wei Chen, Shangfei Zheng & Lei Zhao

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  1. Qian Zhou
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  2. Yihan Hei
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  3. Wei Chen
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  4. Shangfei Zheng
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  5. Lei Zhao
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Qian Zhou and Yihan Hei wrote the main manuscript text, with contributions from other reviewers who validated the content. The manuscript was reviewed by all authors.

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Correspondence to Wei Chen or Lei Zhao.

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Zhou, Q., Hei, Y., Chen, W. et al. MMUIL: enhancing multi-platform user identity linkage with multi-information. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02088-5

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