Estimating the number of communities by spectral methods
Community detection is a fundamental problem in network analysis with many methods
available to estimate communities. Most of these methods assume that the number of …
available to estimate communities. Most of these methods assume that the number of …
Estimating the number of communities in networks by spectral methods
Community detection is a fundamental problem in network analysis with many methods
available to estimate communities. Most of these methods assume that the number of …
available to estimate communities. Most of these methods assume that the number of …
Special invited paper: The SCORE normalization, especially for heterogeneous network and text data
SCORE was introduced as a spectral approach to network community detection. Since many
networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) …
networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) …
Regularized spectral clustering under the mixed membership stochastic block model
H Qing, J Wang - Neurocomputing, 2023 - Elsevier
Mixed membership community detection is a challenging problem in network analysis.
Previous spectral clustering algorithms for this problem are developed based on the …
Previous spectral clustering algorithms for this problem are developed based on the …
Optimal estimation of the number of network communities
In network analysis, how to estimate the number of communities K is a fundamental problem.
We consider a broad setting where we allow severe degree heterogeneity and a wide range …
We consider a broad setting where we allow severe degree heterogeneity and a wide range …
Privacy-preserving community detection for locally distributed multiple networks
Modern multi-layer networks are commonly stored and analyzed in a local and distributed
fashion because of the privacy, ownership, and communication costs. The literature on the …
fashion because of the privacy, ownership, and communication costs. The literature on the …
Estimating graph dimension with cross-validated eigenvalues
In applied multivariate statistics, estimating the number of latent dimensions or the number of
clusters is a fundamental and recurring problem. One common diagnostic is the scree plot …
clusters is a fundamental and recurring problem. One common diagnostic is the scree plot …
Statistical Network Analysis: Past, Present, and Future
S Sengupta - arXiv preprint arXiv:2311.00122, 2023 - arxiv.org
This article provides a brief overview of statistical network analysis, a rapidly evolving field of
statistics, which encompasses statistical models, algorithms, and inferential methods for …
statistics, which encompasses statistical models, algorithms, and inferential methods for …
Randomized spectral clustering in large-scale stochastic block models
Spectral clustering has been one of the widely used methods for community detection in
networks. However, large-scale networks bring computational challenges to the eigenvalue …
networks. However, large-scale networks bring computational challenges to the eigenvalue …
Joint spectral clustering in multilayer degree-corrected stochastic blockmodels
Modern network datasets are often composed of multiple layers, either as different views,
time-varying observations, or independent sample units, resulting in collections of networks …
time-varying observations, or independent sample units, resulting in collections of networks …