Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data
This work addresses identity linkage challenges for social media and LBSN platforms, which is incremental as it builds on existing transformer methods to handle data sparsity and noise.
The paper tackles the problem of cross-platform user identity linkage using heterogeneous mobility data by proposing a transformer-based framework that captures spatio-temporal co-occurrence patterns, resulting in significant performance improvements of 12.92%~17.76% in Macro-F1 and 5.80%~8.38% in AUC over state-of-the-art baselines.
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).