A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
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The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.
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Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
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SoK: Practical Aspects of Releasing Differentially Private Graphs
The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.