Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models

תקציר מקורי באנגליתarXiv:2601.16427v3 Announce Type: replace-cross Abstract: We study exact community recovery in sparse directed stochastic block models using neighborhood smoothing of connection-probability profiles. The proposed method clusters vertices according to their estimated outgoing connection-probability profiles. For each vertex, its complete outgoing profile is estimated by averaging the adjacency rows of empirically similar vertices, after which \(K\)-means is applied to the estimated profiles. An analogous procedure based on incoming connection-probability profiles is obtained by applying the same construction to the transposed adjacency matrix. We establish a finite-sample uniform row-wise error bound for the asymmetric smoothed estimator and derive consistency in the normalized two-to-infin