Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations

Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous it is around the neighborhood area (neighbor spatial correlation), and (3) the closer between two timestamps, the more relevant events are (hawkes temporal correlation). To this end, we propose Hawkes-enhanced Spatial-Temporal Hypergraph Contrastive Learning framework (HCL), which mines the aforementioned correlations via two specific strategies. Concretely, contrastive learning strategies are designed for two spatial correlations, and hawkes process modeling is adopted for temporal correlations. Extensive experiments demonstrate the promising capacities of HCL from four aspects, i.e., superiority, transferability, effectiveness, and sensitivity.

Authors

• Ke Liang School of Computer, National University of Defense Technology, Changsha, China
• Sihang Zhou College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
• Meng Liu School of Computer, National University of Defense Technology
• Yue Liu Institute of Data Science & School of Computing, National University of Singapore
• Wenxuan Tu School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, China
• Yi Zhang Tsinghua University
• Liming Fang Nanjing University of Aeronautics and Astronautics
• Zhe Liu Zhejiang Lab
• Xinwang Liu National University of Defense Technology

Keywords

• DMKM: Graph Mining, Social Network Analysis & Community
• DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data
• ML: Applications