Hierarchical spatio-temporal graph network for risk prediction.

ABSTRACT: Accurate risk prediction remains a critical challenge in reliability engineering and system safety, particularly in complex systems characterized by interdependent temporal progression and spatial co-occurrence patterns. While existing approaches predominantly focus on either temporal dynamics or co-occurrence relationships, this study introduces a novel spatio-temporal graph learning architecture. First, a dual-matrix graph construction mechanism simultaneously captures spatial risk correlations through co-occurrence frequency analysis and temporal progression patterns using transition probability modeling. Second, an adaptive subgraph extraction module generates system-specific topological representations that preserve both localized risk clusters and directed temporal pathways. Third, a

SUBMITTER: Chen F 

PROVIDER: S-EPMC12852903 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature