HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning

• URL: http://arxiv.org/abs/2410.10915v2
• Date: Thu, 14 Aug 2025 05:43:10 GMT
• Title: HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
• Authors: Qianru Zhang, Xinyi Gao, Haixin Wang, Dong Huang, Siu-Ming Yiu, Hongzhi Yin,
• Abstract summary: 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.<n>We propose Hurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation.
• Score: 36.80668790442231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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