Introduces a nonparametric inference procedure based on a sparse signed graphon model that yields valid confidence intervals for balance parameters and reports strong empirical evidence for balance theory across real signed networks.
Subsampling sparse graphons under minimal assumptions
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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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Nonparametric Inference for Balance in Signed Networks
Introduces a nonparametric inference procedure based on a sparse signed graphon model that yields valid confidence intervals for balance parameters and reports strong empirical evidence for balance theory across real signed networks.
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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.