Spectral Embedding of Weighted Graphs

DOI10.1080/01621459.2023.2225239MaRDI QIDQ6631693

Andrew James Jones, Anna Bertiger, C. E. Priebe, Unnamed Author, Patrick Rubin-Delanchy

Publication date: 1 November 2024

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

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Spectral clustering and the high-dimensional stochastic blockmodel
• Bayesian anomaly detection methods for social networks
• Automatic dimensionality selection from the scree plot via the use of profile likelihood
• Limit theorems for eigenvectors of the normalized Laplacian for random graphs
• Optimal rates for community estimation in the weighted stochastic block model
• The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
• Role of normalization in spectral clustering for stochastic blockmodels
• Community structure in social and biological networks
• Statistical inference on random dot product graphs: a survey
• Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
• On a two-truths phenomenon in spectral graph clustering
• Choosing between methods of combining $p$-values
• A General Framework for Weighted Gene Co-Expression Network Analysis
• A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
• Statistical methods for research workers.
• A statistical interpretation of spectral embedding: the generalised random dot product graph
• Spectral Clustering on Spherical Coordinates Under the Degree-Corrected Stochastic Blockmodel

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