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Optimization and Modeling in Spatio-Temporal Data Mining Using Graph Neural...
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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 1222

Special Issue Editors

Dr. Guangyin Jin

E-Mail Website
Guest Editor
1. National Innovative Institute of Defense Technology, Academy of Military Sciences, Beijing, China
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
Dr. Zezhi Shao

E-Mail
Guest Editor
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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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Published Papers (2 papers)

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24 pages, 1855 KB  
Article
Fairness-Aware Optimization in Spatio-Temporal Epidemic Data Mining: A Graph-Augmented Temporal Fusion Transformer
by Saleh Albahli
Mathematics 2026, 14(7), 1179; https://doi.org/10.3390/math14071179 - 1 Apr 2026
Viewed by 255
Abstract
Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, [...] Read more.
Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, and retrieval-augmented generation (RAG) into a single mathematical architecture. The predictive backbone employs a graph-augmented Temporal Fusion Transformer to capture non-linear temporal dependencies and spatial disease propagation. By formalizing regional topology and mobility flows as a weighted mathematical graph, the model systematically integrates structured epidemiological counts, continuous environmental covariates, and digital trace signals. To address algorithmic bias, we formulate a fairness-aware optimization problem by embedding a specific regularization term into the training objective, which mathematically penalizes disparities in true-positive rates across diverse socio-demographic strata. Furthermore, the numerical outputs and anomaly scores are processed by a large language model equipped with hybrid dense and sparse retrieval to generate interpretable, computationally grounded decision support. Extensive experiments on a longitudinal dataset comprising 62 administrative regions over 260 weeks validate the mathematical robustness of the proposed framework. The graph-augmented architecture improved forecasting accuracy by up to 24% and anomaly detection F1 scores by over 6% compared to state-of-the-art deep learning baselines, while the fairness-regularized loss function reduced the maximum subgroup recall gap by more than 50%. These findings demonstrate that predictive accuracy and algorithmic fairness can be jointly optimized, providing a rigorous computational methodology for spatio-temporal epidemic modeling and AI-driven surveillance. Full article
(This article belongs to the Special Issue Optimization and Modeling in Spatio-Temporal Data Mining Using Graph Neural Networks)
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20 pages, 1030 KB  
Article
TFGCRN: Temporal–Frequency Graph Convolutional Recurrent Network for Incomplete Traffic Forecasting
by Jiazhan Hu and Tao Feng
Mathematics 2025, 13(24), 4003; https://doi.org/10.3390/math13244003 - 16 Dec 2025
Viewed by 645
Abstract
Traffic forecasting is a crucial component that underpins an intelligent transportation system. Among the current mainstream forecasting algorithms, spatial–temporal graph neural networks (STGNNs), as the mainstream solution, have been used in traffic forecasting due to their ability to model spatial–temporal dependencies effectively. However, [...] Read more.
Traffic forecasting is a crucial component that underpins an intelligent transportation system. Among the current mainstream forecasting algorithms, spatial–temporal graph neural networks (STGNNs), as the mainstream solution, have been used in traffic forecasting due to their ability to model spatial–temporal dependencies effectively. However, sensor failures caused by factors such as bad weather often lead to incomplete traffic data, which severely prevents STGNNs from modeling spatial–temporal dependencies and consequently degrades forecasting performance. To achieve accurate forecasting under incomplete traffic conditions, this paper proposes a Temporal–Frequency Graph Convolutional Recurrent Network (TFGCRN) model that embeds a Temporal–Frequency Graph Convolutional Network into gated recurrent units. During the recursive modeling process, TFGCRN fully leverages both global and local information to resist the adverse effects of missing values while generating more accurate spatial relationships, thereby achieving precise incomplete traffic forecasting. The experiments on four real-world datasets show that TFGCRN can achieve satisfactory results superior to multiple baselines and effectively adapt to different missing rates. Compared with the state-of-the-art baseline, TFGCRN can reduce forecasting error by 2–6%. Full article
(This article belongs to the Special Issue Optimization and Modeling in Spatio-Temporal Data Mining Using Graph Neural Networks)
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