3 papers
stat.ME2026
Leave-One-Out Neighborhood Smoothing for Graphons: Berry--Esseen Bounds, Confidence Intervals, and Edge-Wise No-Leakage Tuning
Behzad Aalipur, Rachel Kilby
We investigate entrywise uncertainty quantification in graphon models, where existing methods can achieve strong estimation guarantees but often lack tractable distributional theor…
math.PR2026
Distributional Limits for Eigenvalues of Graphon Kernel Matrices
Behzad Aalipur
We study eigenvalue fluctuations of kernel and adjacency matrices in dense graphon-based random graph models, for modes associated with simple, isolated nonzero eigenvalues of the…
stat.ML2026
Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models
Behzad Aalipur, Yichen Qin
We study exact community recovery in sparse directed stochastic block models using neighborhood smoothing of connection-probability profiles. The proposed method clusters vertices…