Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910902403465216 |
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| author | Yan, Ziang Zhao, Xingyu Ma, Hanqing Chen, Wei Qi, Jianpeng Yu, Yanwei Dong, Junyu |
| author_facet | Yan, Ziang Zhao, Xingyu Ma, Hanqing Chen, Wei Qi, Jianpeng Yu, Yanwei Dong, Junyu |
| contents | With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Linkage Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%~17.76% and 5.80%~8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data Yan, Ziang Zhao, Xingyu Ma, Hanqing Chen, Wei Qi, Jianpeng Yu, Yanwei Dong, Junyu Social and Information Networks Artificial Intelligence With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Linkage Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%~17.76% and 5.80%~8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC). |
| title | Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2504.01979 |