HGAurban：用于城市时空学习的异构图自编码
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
AI总结：
提出HGAurban异构时空图掩码自编码器，通过生成式自监督学习联合处理节点特征与图结构，有效克服数据噪声和稀疏性问题，在多项时空挖掘任务中优于现有方法。
摘要：
时空图表示在城市感知应用中发挥着关键作用，包括交通分析、人类移动行为建模和全市犯罪预测。然而，一个关键挑战在于时空数据的噪声和稀疏性，这限制了现有神经网络在时空图中学习有意义的区域表示的能力。为了克服这些局限性，我们提出了HGAurban，一种新颖的异构时空图掩码自编码器，它利用生成式自监督学习来实现稳健的城市数据表示。我们的框架引入了一个时空异构图编码器，从多源数据中提取区域间的依赖关系，从而实现对多样空间关系的全面建模。在我们的自监督学习范式中，我们实现了一个联合处理节点特征和图结构的掩码自编码器。该方法能自动学习跨区域的异构时空模式，显著改善动态时间相关性的表示。在多个时空挖掘任务上的综合实验表明，我们的框架优于最先进的方法，并能稳健地处理现实世界城市数据挑战，包括空间和时间维度上的噪声和稀疏性。
英文摘要：
Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, a key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks' ability to learn meaningful region representations in the spatial-temporal graph. To overcome these limitations, we propose HGAurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation. Our framework introduces a spatial-temporal heterogeneous graph encoder that extracts region-wise dependencies from multi-source data, enabling comprehensive modeling of diverse spatial relationships. Within our self-supervised learning paradigm, we implement a masked autoencoder that jointly processes node features and graph structure. This approach automatically learns heterogeneous spatial-temporal patterns across regions, significantly improving the representation of dynamic temporal correlations. Comprehensive experiments across multiple spatiotemporal mining tasks demonstrate that our framework outperforms state-of-the-art methods and robustly handles real-world urban data challenges, including noise and sparsity in both spatial and temporal dimensions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
