<ul><li>Spatial-temporal data often have missing values, making data analysis challenging.</li><li>Existing methods assume the same spatial relationship for all features across different locations.</li><li>The multi-scale Graph Structure Learning framework for spatial-temporal Imputation (GSLI) dynamically adapts to diverse spatial correlations.</li><li>GSLI incorporates node-scale and feature-scale graph structure learning, prominence modeling, and cross-feature and cross-temporal representation learning.</li></ul>

Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales

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