用知识引导的超网络，让一个模型跨城市预测不同犯罪类型。
Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks
- 用超网络动态生成针对不同犯罪类型的预测参数。
- 在两个城市上测试，跨类型预测效果优于现有方法。
- 适合需要统一建模多城市犯罪的公共安全研究者。
城市犯罪预测对公共安全至关重要，但现有方法难以处理数据差异大的城市间犯罪类型不一致问题。本文提出HYSTL框架，通过设计超网络动态生成预测函数参数，并引入结构化犯罪知识图谱，利用犯罪间的语义关联增强预测能力。该框架无需假设各城市包含相同犯罪类型，可训练出统一且更强的犯罪预测模型。在两个犯罪类型无重叠的城市上进行实验，结果表明，与当前最优基线相比，该方法显著提升预测性能。
原文摘要 · Abstract (English)
Predicting crimes in urban environments is crucial for public safety, yet existing prediction methods often struggle to align the knowledge across diverse cities that vary dramatically in data availability of specific crime types. We propose HYpernetwork-enhanced Spatial Temporal Learning (HYSTL), a framework that can effectively train a unified, stronger crime predictor without assuming identical crime types in different cities' records. In HYSTL, instead of parameterising a dedicated predictor per crime type, a hypernetwork is designed to dynamically generate parameters for the prediction function conditioned on the crime type of interest. To bridge the semantic gap between different crime types, a structured crime knowledge graph is built, where the learned representations of crimes are used as the input to the hypernetwork to facilitate parameter generation. As such, when making predictions for each crime type, the predictor is additionally guided by its intricate association with other relevant crime types. Extensive experiments are performed on two cities with non-overlapping crime types, and the results demonstrate HYSTL outperforms state-of-the-art baselines.
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