Spectral Embedding of Weighted Graphs
From MaRDI portal
Publication:6631693
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)
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
This page was built for publication: Spectral Embedding of Weighted Graphs