{"type":"ScholarlyArticle","title":"Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning","authors":[{"name":"Lianghao Xia","slug":"lianghao-xia","given":"Lianghao","family":"Xia","affiliation":""},{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":""},{"name":"Yong Xu","slug":"yong-xu","given":"Yong","family":"Xu","affiliation":""},{"name":"Peng Dai","slug":"peng-dai","given":"Peng","family":"Dai","affiliation":""},{"name":"Liefeng Bo","slug":"liefeng-bo","given":"Liefeng","family":"Bo","affiliation":""},{"name":"Xiyue Zhang","slug":"xiyue-zhang","given":"Xiyue","family":"Zhang","affiliation":""},{"name":"Tianyi Chen","slug":"tianyi-chen","given":"Tianyi","family":"Chen","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/225","url":"https://ijcai.org/proceedings/2021/225","pdf":"https://ijcai.org/proceedings/2021/0225.pdf","markdown":"https://ijcai.org/proceedings/2021/225.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/225","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":225,"pages":{"first":1631,"last":1637},"keywords":["Data Mining: Mining Spatial, Temporal Data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}