DOI10.1111/rssb.12117zbMath1411.62162arXiv1311.2694OpenAlexW2963678301WikidataQ105584089 ScholiaQ105584089MaRDI QIDQ5743234
Peter J. Bickel, Purnamrita Sarkar
Publication date: 9 May 2019
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1311.2694
Testing community structure for hypergraphs,
The Bethe Hessian and information theoretic approaches for online change-point detection in network data,
Scalable estimation of epidemic thresholds via node sampling,
A likelihood-ratio type test for stochastic block models with bounded degrees,
Hierarchical Community Detection by Recursive Partitioning,
Goodness-of-fit test for latent block models,
Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral estimator,
Local law and Tracy-Widom limit for sparse random matrices,
Hypothesis testing in sparse weighted stochastic block model,
Local law and Tracy-Widom limit for sparse stochastic block models,
Using Maximum Entry-Wise Deviation to Test the Goodness of Fit for Stochastic Block Models,
A goodness-of-fit test on the number of biclusters in a relational data matrix,
Classified generalized linear mixed model prediction incorporating pseudo‐prior information,
Pseudo-Bayesian Classified Mixed Model Prediction,
Directed Community Detection With Network Embedding,
Unnamed Item,
Universal rank inference via residual subsampling with application to large networks,
Quantitative Tracy-Widom laws for the largest eigenvalue of generalized Wigner matrices,
On the limiting spectral distributions of stochastic block models,
A Spectral-Based Framework for Hypothesis Testing in Populations of Networks,
A practical two-sample test for weighted random graphs,
Asymptotic uncertainty quantification for communities in sparse planted bi-section models,
Statistical embedding: beyond principal components,
Limiting spectral distribution of stochastic block model,
Core-periphery structure in networks: a statistical exposition,
Bayesian estimation of the latent dimension and communities in stochastic blockmodels,
Consistent Estimation of the Number of Communities via Regularized Network Embedding,
Corrected Bayesian Information Criterion for Stochastic Block Models,
Computing exact \(p\)-values for community detection,
Spectral based hypothesis testing for community detection in complex networks,
Estimating the number of communities by spectral methods,
Adjusted chi-square test for degree-corrected block models,
Adjacency matrix comparison for stochastic block models,
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs,
Local law and Tracy-Widom limit for sparse sample covariance matrices,
Minimax rates in network analysis: graphon estimation, community detection and hypothesis testing,
Nonreconstruction of high-dimensional stochastic block model with bounded degree,
Selective inference for latent block models,
Network Cross-Validation for Determining the Number of Communities in Network Data,
Node Features Adjusted Stochastic Block Model,
Network Modularity in the Presence of Covariates,
Community detection on mixture multilayer networks via regularized tensor decomposition,
Optimal adaptivity of signed-polygon statistics for network testing,
Unnamed Item,
Unnamed Item,
Detecting Overlapping Communities in Networks Using Spectral Methods
