# Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data

arXiv: https://arxiv.org/abs/2504.01979 · HTML: https://mlsanity.com/paper/2504.01979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://mlsanity.com/paper/2402.19072/) (2024)
- [MST: Masked Self-Supervised Transformer for Visual Representation](https://mlsanity.com/paper/2106.05656/) (2021)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction](https://mlsanity.com/paper/2006.01963/) (2020)
- [Mutual Distillation Learning Network for Trajectory-User Linking](https://mlsanity.com/paper/2205.03773/) (2022)
- [Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks](https://mlsanity.com/paper/2302.10903/) (2023)
- [Where You Are Is Who You Are: User Identification by Matching Statistics](https://mlsanity.com/paper/1512.02896/) (2015)
- [Spatio-Temporal Linkage over Location Enhanced Services](https://mlsanity.com/paper/1801.04101/) (2018)
- [HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks](https://mlsanity.com/paper/2201.02830/) (2022)
- [DegUIL: Degree-aware Graph Neural Networks for Long-tailed User Identity Linkage](https://mlsanity.com/paper/2308.05322/) (2023)

## Abstract (arXiv)

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).