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.
Bickel, Aiyou Chen, and Elizaveta Levina
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Nonparametric inference for structural balance in dynamic signed networks via time-varying graphon model, kernel smoothing, and Edgeworth expansion for studentized statistics.
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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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Inference for Balance in Dynamic Signed Networks
Nonparametric inference for structural balance in dynamic signed networks via time-varying graphon model, kernel smoothing, and Edgeworth expansion for studentized statistics.