scientific article; zbMATH DE number 7626709
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Publication:5053174
Fred Roosta, Minh Tang, Keith Levin, Michael W. Mahoney, Carey E. Priebe
Publication date: 6 December 2022
Full work available at URL: https://arxiv.org/abs/1910.00423
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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- A limit theorem for scaled eigenvectors of random dot product graphs
- Universally consistent vertex classification for latent positions graphs
- Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
- Automatic dimensionality selection from the scree plot via the use of profile likelihood
- The out-of-sample problem for classical multidimensional scaling
- Limit theorems for eigenvectors of the normalized Laplacian for random graphs
- Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral estimator
- Distributed estimation of principal eigenspaces
- Diffusion maps
- Modern multidimensional scaling. Theory and applications.
- Multidimensional scaling. I: Theory and method
- Emergence of Scaling in Random Networks
- Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
- Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
- Procrustes Problems
- Latent Space Approaches to Social Network Analysis
- Statistical inference on random dot product graphs: a survey
- A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Optimal Bayesian estimation for random dot product graphs
- A useful variant of the Davis–Kahan theorem for statisticians
- Random Dot Product Graph Models for Social Networks
- An Introduction to Matrix Concentration Inequalities
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