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

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