Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
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2 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A multi-stage smoothing estimator is developed to estimate time-varying network edge probabilities under Hölder smoothness and piecewise Lipschitz conditions.
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Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
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Nonparametric estimation of time-varying network connections by multi-stage smoothing
A multi-stage smoothing estimator is developed to estimate time-varying network edge probabilities under Hölder smoothness and piecewise Lipschitz conditions.