A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.
A tutorial on spectral clustering.Statistics and Computing, 17(4):395–416
2 Pith papers cite this work. Polarity classification is still indexing.
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Prism defines a duality defect δ(L,P) to measure structural symmetry deviation in graphs and reports it detects rising stress in S&P 500 networks before correlation spikes appear.
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Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency
A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.
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Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection
Prism defines a duality defect δ(L,P) to measure structural symmetry deviation in graphs and reports it detects rising stress in S&P 500 networks before correlation spikes appear.