Summary of Spatial-temporal Mixture-of-graph-experts For Multi-type Crime Prediction, by Ziyang Wu et al.
Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction
by Ziyang Wu, Fan Liu, Jindong Han, Yuxuan Liang, Hao Liu
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for predicting multiple types of crimes, accounting for their spatial-temporal heterogeneity. The Spatial-Temporal Mixture-of-Graph-Experts (ST-MoGE) approach utilizes attentive-gated Mixture-of-Graph-Experts (MGEs) to capture distinctive and shared crime patterns across different categories. To mitigate imbalanced spatial distribution, the framework incorporates Hierarchical Adaptive Loss Re-weighting (HALR). The authors conduct experiments on two real-world crime datasets, comparing their results with 12 advanced baselines. The findings demonstrate the superiority of ST-MoGE in predicting collective crime occurrences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict when different types of crimes will happen, which is important for keeping people safe and healthy. Current methods don’t consider how different crime categories are spread out in space and time. The new framework, called Spatial-Temporal Mixture-of-Graph-Experts (ST-MoGE), can learn about each type of crime separately and combine that information to make better predictions. |
