Spectral graph clustering via the expectation-solution algorithm

DOI10.1214/22-EJS2018zbMath1493.62397arXiv2003.13462OpenAlexW3013783389MaRDI QIDQ2154947

Joshua S. Agterberg, Zachary M. Pisano, Daniel Q. Naiman, Carey E. Priebe

Publication date: 15 July 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.13462

zbMATH Keywords

EM algorithmrandom graphestimating equationsmixture modelcurved exponential family

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Random matrices (probabilistic aspects) (60B20)

Cites Work

• Unnamed Item
• Unnamed Item
• A limit theorem for scaled eigenvectors of random dot product graphs
• Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
• Estimating the dimension of a model
• Limit theorems for eigenvectors of the normalized Laplacian for random graphs
• The EM Algorithm and Extensions, 2E
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Statistical inference on random dot product graphs: a survey
• A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs
• Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
• On a two-truths phenomenon in spectral graph clustering
• Consistent Adjacency-Spectral Partitioning for the Stochastic Block Model When the Model Parameters Are Unknown
• Random Dot Product Graph Models for Social Networks
• On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
• A new look at the statistical model identification

This page was built for publication: Spectral graph clustering via the expectation-solution algorithm