Bias-Adjusted Spectral Clustering in Multi-Layer Stochastic Block Models

DOI10.1080/01621459.2022.2054817arXiv2003.08222OpenAlexW3135270353MaRDI QIDQ6144759

Kevin Lin, Jing Lei

Publication date: 8 January 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.08222

zbMATH Keywords

community detectionnetwork dataspectral clusteringmatrix concentration inequalitiesstochastic block modelsgene co-expression network

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Spectral Clustering via Adaptive Layer Aggregation for Multi-Layer Networks ⋮ Covariate-Assisted Community Detection in Multi-Layer Networks

Cites Work

• Unnamed Item
• Partial distance correlation with methods for dissimilarities
• Fast community detection by SCORE
• Network cross-validation by edge sampling
• Network Cross-Validation for Determining the Number of Communities in Network Data
• Sharp nonasymptotic bounds on the norm of random matrices with independent entries
• Minimax rates of community detection in stochastic block models
• Spectral clustering and the high-dimensional stochastic blockmodel
• Spectral clustering in the dynamic stochastic block model
• User-friendly tail bounds for sums of random matrices
• Spectral norm of products of random and deterministic matrices
• Hanson-Wright inequality and sub-Gaussian concentration
• Statistical analysis of network data. Methods and models
• Spectral analysis of large dimensional random matrices
• Random perturbation of low rank matrices: improving classical bounds
• A proof of the block model threshold conjecture
• Decoupling inequalities for the tail probabilities of multivariate \(U\)- statistics
• Weak convergence and empirical processes. With applications to statistics
• Sharp optimal recovery in the two component Gaussian mixture model
• Heteroskedastic PCA: algorithm, optimality, and applications
• Spectral and matrix factorization methods for consistent community detection in multi-layer networks
• Consistency of spectral clustering in stochastic block models
• The Kato-Temple inequality and eigenvalue concentration with applications to graph inference
• A nonparametric view of network models and Newman–Girvan and other modularities
• Mixed membership stochastic blockmodels
• Community Detection and Stochastic Block Models
• Tensor SVD: Statistical and Computational Limits
• Clustering With Multi-Layer Graphs: A Spectral Perspective
• Global spectral clustering in dynamic networks
• A Survey of Statistical Network Models
• Variational Bayesian inference and complexity control for stochastic block models
• Consistent community detection in multi-layer network data
• Community detection thresholds and the weak Ramanujan property
• Spectral techniques applied to sparse random graphs
• Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model
• Concentration and regularization of random graphs
• A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables

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