Role of normalization in spectral clustering for stochastic blockmodels
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Publication:2352733
DOI10.1214/14-AOS1285zbMath1320.62150arXiv1310.1495MaRDI QIDQ2352733
Purnamrita Sarkar, Peter J. Bickel
Publication date: 6 July 2015
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1310.1495
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random matrices (probabilistic aspects) (60B20)
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Cites Work
- Unnamed Item
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- Pseudo-likelihood methods for community detection in large sparse networks
- Impact of regularization on spectral clustering
- On the spectra of general random graphs
- Spectral clustering and the high-dimensional stochastic blockmodel
- The eigenvalues of random symmetric matrices
- Role of normalization in spectral clustering for stochastic blockmodels
- Consistency of spectral clustering
- A new status index derived from sociometric analysis
- A nonparametric view of network models and Newman–Girvan and other modularities
- Partitioning Sparse Matrices with Eigenvectors of Graphs
- A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs
- An Improved Spectral Graph Partitioning Algorithm for Mapping Parallel Computations
- Spectral techniques applied to sparse random graphs
- Lower Bounds for the Partitioning of Graphs