Degrading the accuracy of interlayer link prediction: A method based on the analysis of node importance

Rui Tang, Ziyun Yong, Yiguo Mei, Xiangze Li, Jingxi Li, Junkai Ding and Xian Mo
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Rui Tang: School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, P. R. China2State Key Laboratory of Cryptology, Beijing 100878, China
Ziyun Yong: School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, P. R. China2State Key Laboratory of Cryptology, Beijing 100878, China
Yiguo Mei: Huaxin Consulting and Designing Institute Co. Ltd., Hangzhou 310052, P. R. China
Xiangze Li: School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, P. R. China
Jingxi Li: School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, P. R. China
Junkai Ding: Faculty of Computer Science and Information Technology, University Malaya, Kuala Lumpur 50603, Malaysia
Xian Mo: School of Information Engineering, Ningxia University, Yinchuan 750021, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 10, 1-26

Abstract: Interlayer link prediction aims to identify the same entities in multiplex networks, providing significant value for applications like social network analysis and personalized recommendations. However, malicious individuals can misuse it to aggregate users’ private information. To address this, social network platforms must explore preventive measures, such as useful information deletion or noise information addition, to attack successful interlayer link prediction. This study introduces a novel node importance-based network structure perturbation method to degrade the accuracy of interlayer link prediction algorithms hence protecting user privacy. It utilizes the “guessing the friends†game analogy to identify influential nodes in social networks based on degree centrality. Perturbation involves randomly removing connections from nodes with low, medium, and high degree centrality. Multiple interlayer link prediction algorithms are evaluated under these perturbation attacks to assess their impact. Experiments on real-world datasets validate the proposed method’s effectiveness and reveal that perturbing the nodes with low degree centrality significantly affects interlayer link prediction accuracy. This research contributes valuable insights into safeguarding user privacy in interlayer link prediction, offering a robust approach anchored in node importance considerations. It promises to advance interlayer link prediction techniques and protect social network user privacy effectively.

Keywords: Iterlayer link prediction; social network; multiplex network; structural perturbation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S012918312442004X

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