{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T23:36:58Z","timestamp":1775000218020,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/225","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"1631-1637","source":"Crossref","is-referenced-by-count":54,"title":["Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning"],"prefix":"10.24963","author":[{"given":"Lianghao","family":"Xia","sequence":"first","affiliation":[{"name":"South China University of Technology, China"}]},{"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"},{"name":"Communication and Computer Network Laboratory of Guangdong, China"},{"name":"Peng Cheng Laboratory, China"}]},{"given":"Peng","family":"Dai","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation, USA"}]},{"given":"Liefeng","family":"Bo","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation, USA"}]},{"given":"Xiyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}]},{"given":"Tianyi","family":"Chen","sequence":"additional","affiliation":[{"name":"South China University of Technology, China"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:02:06Z","timestamp":1628679726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/225"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/225","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}