HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting-猫眼课题宝

DOI：

10.1609/aaai.v36i4.20338

发表时间：

2021-09

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作者：

Chenyu Wang;Zongyu Lin;Xiaochen Yang;Jiao Sun;Mingxuan Yue;C. Shahabi

犯罪预测问题的目标是预测在不久的将来每个地理区域（如社区或审查区）的不同类型的犯罪。由于邻近区域通常具有类似的社会经济特征，这表明类似的犯罪模式，最近的最先进的解决方案构建了一个基于距离的区域图，并利用图神经网络（GNN）技术进行犯罪预测，因为GNN技术可以有效地利用图中相邻区域节点之间的潜在关系，如果边缘显示出高度的依赖性或相关性。然而，这种基于距离的预定义图不能完全捕获彼此远离但具有相似犯罪模式的区域之间的犯罪相关性。因此，为了做出更准确的犯罪预测，主要的挑战是学习一个更好的图，揭示犯罪发生区域之间的依赖关系，同时从历史犯罪记录中捕捉时间模式。为了解决这些挑战，我们提出了一个端到端的图卷积递归网络哈根，它具有几种用于犯罪预测的新颖设计。具体来说，我们的框架可以通过将自适应区域图学习模块与扩散卷积门控递归单元（DCGRU）相结合，共同捕获区域之间的犯罪相关性和时间犯罪动态。基于GNN的同质性假设（即，图卷积在相邻节点共享相同标签的情况下工作得更好），我们提出了一个homophily-aware约束来正则化区域图的优化，使得学习图上的相邻区域节点共享相似的犯罪模式，从而拟合扩散卷积的机制。在两个真实数据集上的实证实验和综合分析表明了哈根的有效性。

The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.

HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

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