HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
This work addresses urban sensing challenges for applications such as traffic and crime prediction, but it appears incremental as it builds on existing graph autoencoding techniques.
The paper tackled the problem of noisy and sparse spatial-temporal data in urban sensing by proposing HGAurban, a heterogeneous graph masked autoencoder, which outperformed state-of-the-art methods in tasks like traffic analysis and crime prediction.
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.