Optimization and Modeling in Spatio-Temporal Data Mining Using Graph Neural Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 41
Special Issue Editors
2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: spatio-temporal correlations; computational modeling; travel time estimation; graph neural networks; time series analysis
Interests: spatial-temporal data mining; time series forecasting; graph data mining
Special Issue Information
Dear Colleagues,
Spatio-temporal data mining involves the analysis of data that evolves over both spatial and temporal dimensions, characterized by complex interactions, heterogeneity in data sources, and dynamic patterns across geographic and time-related features. Traditional approaches often struggle to effectively capture these dual dependencies due to limitations in modeling spatial relationships and temporal sequences simultaneously. However, the rise of Graph Neural Networks (GNNs) has introduced a promising paradigm for representing spatio-temporal systems as dynamic graphs, where spatial entities are encoded as nodes and temporal interactions as edges or temporal graph structures. Recent advances in GNNs have enabled sophisticated modeling of spatial dependencies, temporal dynamics, and their interplay, offering new opportunities for tasks such as trajectory prediction, spatial anomaly detection, and urban mobility analysis. Despite this progress, challenges remain in optimizing GNN-based models for large-scale spatio-temporal data, including efficient handling of temporal evolution, heterogeneous data integration, and the design of robust training frameworks. Furthermore, the mathematical formalization of spatio-temporal graph representations and the development of interpretable models for complex patterns are critical research frontiers. This Special Issue aims to address these gaps by exploring cutting-edge methodologies, theoretical foundations, and practical applications of GNNs in spatio-temporal data mining. Contributions will focus on innovative optimization strategies, hybrid modeling techniques, and scalable architectures tailored to the unique properties of spatio-temporal data, thereby advancing the field of intelligent data analysis in spatial and temporal domains.
Dr. Guangyin Jin
Dr. Zezhi Shao
Guest Editors
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Keywords
- spatio-temporal data mining
- graph neural networks
- dynamic graph modeling
- spatio-temporal graph
- geographic information systems
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