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Regular Paper

Incremental User Identification Across Social Networks Based on User-Guider Similarity Index

School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China
School of Information, Liaoning University, Shenyang 110036, China
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Abstract

Identifying accounts across different online social networks that belong to the same user has attracted extensive attentions. However, existing techniques rely on given user seeds and ignore the dynamic changes of online social networks, which fails to generate high quality identification results. In order to solve this problem, we propose an incremental user identification method based on user-guider similarity index (called CURIOUS), which efficiently identifies users and well captures the changes of user features over time. Specifically, we first construct a novel user-guider similarity index (called USI) to speed up the matching between users. Second we propose a two-phase user identification strategy consisting of USI-based bidirectional user matching and seed-based user matching, which is effective even for incomplete networks. Finally, we propose incremental maintenance for both USI and the identification results, which dynamically captures the instant states of social networks. We conduct experimental studies based on three real-world social networks. The experiments demonstrate the effectiveness and the efficiency of our proposed method in comparison with traditional methods. Compared with the traditional methods, our method improves precision, recall and rank score by an average of 0.19, 0.16 and 0.09 respectively, and reduces the time cost by an average of 81%.

Keywords

user identificationsocial networkuser-guider similarity indexincremental maintenance

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Journal of Computer Science and Technology
Volume 37 Issue 5,
September 2022
Pages 1086-1104
DOI: 10.1007/s11390-022-2430-0
Cite this article:
Kou Y, Li D, Shen D-R, et al. Incremental User Identification Across Social Networks Based on User-Guider Similarity Index. Journal of Computer Science and Technology, 2022, 37(5): 1086-1104. https://doi.org/10.1007/s11390-022-2430-0

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Received: 15 April 2022
Accepted: 15 September 2022
Published: 30 September 2022
©Institute of Computing Technology, Chinese Academy of Sciences 2022
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