# Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

arXiv: https://arxiv.org/abs/2302.10903 · HTML: https://mlsanity.com/paper/2302.10903/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 11 Feb 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification](https://mlsanity.com/paper/1602.02068/) (2016)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Sparse Sequence-to-Sequence Models](https://mlsanity.com/paper/1905.05702/) (2019)
- [Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context](https://mlsanity.com/paper/1805.04623/) (2018)
- [Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation](https://mlsanity.com/paper/1907.08489/) (2019)
- [Mutual Distillation Learning Network for Trajectory-User Linking](https://mlsanity.com/paper/2205.03773/) (2022)
- [Trajectory-Based Spatiotemporal Entity Linking](https://mlsanity.com/paper/2010.01516/) (2020)

## Built on by (most cited first, 7 of 7)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction](https://mlsanity.com/paper/2403.16831/) (2024)
- [TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability](https://mlsanity.com/paper/2408.15251/) (2024)
- [Scalable Trajectory-User Linking with Dual-Stream Representation Networks](https://mlsanity.com/paper/2503.15002/) (2025)
- [Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data](https://mlsanity.com/paper/2504.01979/) (2025)
- [FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning](https://mlsanity.com/paper/2505.05155/) (2025)
- [TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond](https://mlsanity.com/paper/2605.06906/) (2026)

## Abstract (arXiv)

Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framework to encode the temporal dependencies in trajectories, have fall short in capturing spatial-temporal global context for TUL prediction. To ill this gap, this work presents a new hierarchical spatio-temporal attention neural network, called AttnTUL, to jointly encode the local trajectory transitional patterns and global spatial dependencies for TUL. Speciically, our irst model component is built over the graph neural architecture to preserve the local and global context and enhance the representation paradigm of geographical regions and user trajectories. Additionally, a hierarchically structured attention network is designed to simultaneously encode the intra-trajectory and inter-trajectory dependencies, with the integration of the temporal attention mechanism and global elastic attentional encoder. Extensive experiments demonstrate the superiority of our AttnTUL method as compared to state-of-the-art baselines on various trajectory datasets. The source code of our model is available at https://github.com/Onedean/AttnTUL.