Abstract
Cross-social network anchor link prediction plays a pivotal role in downstream tasks, such as comprehensively portraying user characteristics, user friend recommendations, and online public opinion analysis, which aims to find accounts that belong to the same natural person on different social networks. It is a common method to use manually marked anchors or anchors inferred through autonomous learning as supervisory information to guide the prediction of subsequent anchor links. However, the credibility of the anchor is not discussed. In this paper, to address this problem, we propose a new framework that can simultaneously complete the identification of trusted anchors and the prediction of anchor links across social networks under a unified framework. The proposed method can effectively identify non-trusted anchor links and improve the accuracy of the anchor link prediction model through the reconstruction of trusted anchors. Extensive experiments have been conducted on two large-scale real-life social networks. The experimental results demonstrate that the proposed method outperforms the state-of-the-art models with a big margin.
Supported by the National Key R &D Plan project of China (2021YFB3100600), the Youth Innovation Promotion Association, Chinese Academy of Sciences (No.2020163) and the Strategic Pilot Science and Technology Project of Chinese Academy of Sciences (XDC02040400).
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Acknowledgment
This work is supported by the National Key R &D Plan project of China (2021YFB3100600), the Youth Innovation Promotion Association, Chinese Academy of Sciences (No.2020163) and the Strategic Pilot Science and Technology Project of Chinese Academy of Sciences (XDC02040400). We thank all authors for their contributions and all anonymous reviewers for their constructive comments.
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Zhu, D., Xu, Y., Zhang, L., Tang, M., Zhu, W., Xu, H. (2023). Anchor Link Prediction Based on Trusted Anchor Re-identification. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_8
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