# Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

arXiv: https://arxiv.org/abs/2204.08587 · HTML: https://mlsanity.com/paper/2204.08587/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 57 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [Knowledge-aware Coupled Graph Neural Network for Social Recommendation](https://mlsanity.com/paper/2110.03987/) (2021)
- [Quantifying Uncertainty in Deep Spatiotemporal Forecasting](https://mlsanity.com/paper/2105.11982/) (2021)
- [Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks](https://mlsanity.com/paper/1905.08865/) (2019)
- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning](https://mlsanity.com/paper/2201.02435/) (2022)
- [UrbanFM: Inferring Fine-Grained Urban Flows](https://mlsanity.com/paper/1902.05377/) (2019)
- [Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task](https://mlsanity.com/paper/2010.05141/) (2020)
- [Recent Advances in Heterogeneous Relation Learning for Recommendation](https://mlsanity.com/paper/2110.03455/) (2021)
- [Self-supervised and Supervised Joint Training for Resource-rich Machine Translation](https://mlsanity.com/paper/2106.04060/) (2021)

## Built on by (most cited first, 30 of 39)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction](https://mlsanity.com/paper/2408.10269/) (2024)
- [Automated Spatio-Temporal Graph Contrastive Learning](https://mlsanity.com/paper/2305.03920/) (2023)
- [A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide](https://mlsanity.com/paper/2404.01039/) (2024)
- [FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction](https://mlsanity.com/paper/2405.17898/) (2024)
- [BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis](https://mlsanity.com/paper/2412.00953/) (2024)
- [Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning](https://mlsanity.com/paper/2408.12116/) (2024)
- [HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting](https://mlsanity.com/paper/2505.17431/) (2025)
- [Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting](https://mlsanity.com/paper/2309.12028/) (2023)
- [GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2311.04245/) (2023)
- [Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation](https://mlsanity.com/paper/2306.10683/) (2023)
- [Explainable Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2310.17149/) (2023)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [Urban Region Representation Learning with Attentive Fusion](https://mlsanity.com/paper/2312.04606/) (2023)
- [Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://mlsanity.com/paper/2309.11284/) (2023)
- [Spatio-Temporal Meta Contrastive Learning](https://mlsanity.com/paper/2310.17678/) (2023)
- [AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting](https://mlsanity.com/paper/2409.16586/) (2024)
- [Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review](https://mlsanity.com/paper/2506.01364/) (2025)
- [ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2404.07919/) (2024)
- [Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2303.16310/) (2023)
- [UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction](https://mlsanity.com/paper/2508.08551/) (2025)
- [MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2308.15840/) (2023)
- [Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks](https://mlsanity.com/paper/2408.04193/) (2024)
- [Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction](https://mlsanity.com/paper/2409.15764/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data](https://mlsanity.com/paper/2310.00270/) (2023)
- [CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction](https://mlsanity.com/paper/2404.06756/) (2024)
- [Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts](https://mlsanity.com/paper/2412.05534/) (2024)

## Abstract (arXiv)

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devoted to proposing various spatial-temporal forecasting techniques to explore dependence across locations and time periods, most of them follow a supervised learning manner, which limits their spatial-temporal representation ability on sparse crime data. Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination. We perform extensive experiments on two real-life crime datasets. Evaluation results show that our ST-HSL significantly outperforms state-of-the-art baselines. Further analysis provides insights into the superiority of our ST-HSL method in the representation of spatial-temporal crime patterns. The implementation code is available at https://github.com/LZH-YS1998/STHSL.