Carey E Priebe

Biographic Data

ID1911504
NAMECarey E Priebe
GIVEN NAMESCarey E
FAMILY NAMEPriebe
SIGNATUREPRIEBE C E
AFFILIATIONSJohns Hopkins University
ORCID0000-0002-0139-7201
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS5
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2019
LATEST PUBLICATION YEAR2025
H-INDEX1

  • Refined graph encoder embedding via self-training and latent community recovery

    Open AccessCencheng Shen, Jonathan Larson et al.ARTICLESocial Network Analysis and Mining2025

    This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful hidden communities under stochastic block models. Furthermore, we show how the refinement…

  • Spectral Estimation of Large Stochastic Blockmodels with Discrete Nodal Covariates

    Angelo Mele, Ling-Xin Hao et al.ARTICLEJournal of Business and Economic…2023

    In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM). We develop a spectral estimator for the effect of covariates on link probabilities, exploiting the correspondence of SBMs and generalized random dot prod…

  • Sampling methods for archaeological predictive modeling

    Open AccessJacob A Comer, Douglas C Comer et al.ARTICLEJournal of Archaeological Science…2023References: 9

  • Aksumite Settlement Patterns

    Open AccessMichael J Harrower, Joseph C Mazzariello et al.ARTICLEJournal of Archaeological Research2022Cited by: 5References: 27

  • On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs

    Open AccessJoshua Cape, Minh Tang et al.PREPRINTNetwork Science2019References: 2

    Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic blockmodel graphs by way of Chernoff information. We investigate…