Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities
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Abstract (arXiv)
Modern cities are increasingly reliant on data-driven insights to support decision making in areas such as transportation, public safety and environmental impact. However, city-level data often exists in heterogeneous formats, collected independently by local agencies with diverse objectives and standards. Despite their numerous, wide-ranging, and uniformly consumable nature, national-level datasets exhibit significant heterogeneity and multi-modality. This research proposes a heterogeneous data pipeline that performs cross-domain data fusion over time-varying, spatial-varying and spatial-varying time-series datasets. We aim to address complex urban problems across multiple domains and localities by harnessing the rich information over 50 data sources. Specifically, our data-learning module integrates homophily from spatial-varying dataset into graph-learning, embedding information of various localities into models. We demonstrate the generalizability and flexibility of the framework through five real-world observations using a variety of publicly accessible datasets (e.g., ride-share, traffic crash, and crime reports) collected from multiple cities. The results show that our proposed framework demonstrates strong predictive performance while requiring minimal reconfiguration when transferred to new localities or domains. This research advances the goal of building data-informed urban systems in a scalable way, addressing one of the most pressing challenges in smart city analytics.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 11 Dec 2025.
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References · 19
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
- 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
- 2022SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observationcited by 82
- 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
- 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
- 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
- 2021HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecastingcited by 22
- 2023Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learningcited by 20
- 2023Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Predictioncited by 8
- 2019Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecastingcited by 7
7 more
- 2023Travel Demand Forecasting: A Fair AI Approachcited by 7
- 2023Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecastingcited by 6
- 2020AIST: An Interpretable Attention-based Deep Learning Model for Crime Predictioncited by 5
- 2024Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networkscited by 5
- 2024SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networkscited by 4
- 2024Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embeddingcited by 2
- 2023Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfirescited by 1
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