Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
Eigenvector fluctuations and limit results for random graphs with infinite rank kernels
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
3
Pith papers citing it
2
external citations · external index
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.
Vertex misalignment impairs changepoint localization in network time series when the signal is in joint distributions of latent positions, and graph matching or optimal transport cannot correct the impairment.
citing papers explorer
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Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
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Two-Sample Hypothesis Testing for Subspace Equality in Network Data
A two-sample test for subspace equality in networks uses the Frobenius norm of projection matrix differences, with proven asymptotic normality to Gaussian under logarithmic average degree growth.
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Vertex misalignment and changepoint localization in network time series
Vertex misalignment impairs changepoint localization in network time series when the signal is in joint distributions of latent positions, and graph matching or optimal transport cannot correct the impairment.