Spatio-Temporal Data

Overview of Graph Neural Networks: Dynamic Graphs

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Overview of Graph Neural Networks: Dynamic Graphs

Introduction Graph neural networks (GNNs) have been widely applied to the modeling and representation learning of graph-structured data. However, mainstream research has been limited to handling static network data, while real complex networks often undergo structural and property evolution over time. The team led by Katarzyna at the University of Technology Sydney recently published a … Read more

Dynamic Localization of Spatio-Temporal Graph Neural Networks

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Dynamic Localization of Spatio-Temporal Graph Neural Networks

Spatio-temporal data is the foundation of many intelligent applications, revealing the causal relationships between measurements at a specific location and historical data from the same or other locations. In this context, Adaptive Spatio-Temporal Graph Neural Networks (ASTGNNs) have emerged as a powerful tool for modeling these dependencies, particularly through data-driven approaches rather than predefined spatial … Read more