Model-based clustering for random hypergraphs

DOI10.1007/s11634-021-00454-7OpenAlexW3175874043MaRDI QIDQ2103857

Thomas Brendan Murphy, Tin Lok James Ng

Publication date: 9 December 2022

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.05185

zbMATH Keywords

hypergraphclusteringlatent class analysisminorization maximization

Mathematics Subject Classification ID

Multivariate analysis (62H99) Applications of statistics to social sciences (62P25)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Overlapping stochastic block models with application to the French political blogosphere
• Clustering criteria for discrete data and latent class models
• Estimating the dimension of a model
• The phase transition in a random hypergraph
• Recommender systems research: a connection-centric survey
• Phase transition of random non-uniform hypergraphs
• On the strength of connectedness of a random hypergraph
• On the chromatic number of a random hypergraph
• Critical random hypergraphs: the emergence of a giant set of identifiable vertices
• Maximum likelihood estimation via the ECM algorithm: A general framework
• $\beta$ models for random hypergraphs with a given degree sequence
• An Exponential Family of Probability Distributions for Directed Graphs
• Estimation and Prediction for Stochastic Blockstructures
• Latent Space Approaches to Social Network Analysis
• Exploratory latent structure analysis using both identifiable and unidentifiable models