---
title: "Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning"
authors:
  - "Lianghao Xia"
  - "Chao Huang"
  - "Yong Xu"
  - "Peng Dai"
  - "Liefeng Bo"
  - "Xiyue Zhang"
  - "Tianyi Chen"
doi: "10.24963/ijcai.2021/225"
url: "https://ijcai.org/proceedings/2021/225"
pdf: "https://ijcai.org/proceedings/2021/0225.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "1631-1637"
track: "Main Track"
keywords:
  - "Data Mining: Mining Spatial, Temporal Data"
---

# Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, Tianyi Chen

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 1631-1637. DOI: [10.24963/ijcai.2021/225](https://doi.org/10.24963/ijcai.2021/225)

## Abstract

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes. To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wise crime semantic relationships. In specific, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture with the integration of the hypergraph learning paradigm. To capture category-wise crime heterogeneous relations in a dynamic environment, we introduce a multi-channel routing mechanism to learn the time-evolving structural dependency across crime types. We conduct extensive experiments on two real-word datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance as compared to various state-of-the-art baselines. The source code is available at https://github.com/akaxlh/ST-SHN.