HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913989619875840 |
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| author | Zhang, Qianru Gao, Xinyi Wang, Haixin Huang, Dong Yiu, Siu-Ming Yin, Hongzhi |
| author_facet | Zhang, Qianru Gao, Xinyi Wang, Haixin Huang, Dong Yiu, Siu-Ming Yin, Hongzhi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning Zhang, Qianru Gao, Xinyi Wang, Haixin Huang, Dong Yiu, Siu-Ming Yin, Hongzhi Machine Learning 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. |
| title | HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.10915 |