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.
Joint spectral clustering in multilayer degree-corrected stochastic blockmodels
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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.