Identifying graph clusters using variational inference and links to covariance parametrization

DOI10.1098/rsta.2009.0117zbMath1185.05144OpenAlexW2110023275WikidataQ51787295 ScholiaQ51787295MaRDI QIDQ3559953

David Barber

Publication date: 8 May 2010

Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1098/rsta.2009.0117

zbMATH Keywords

networkcovariancevariational inferencegraph clusteringcommunity identificationclique matrix

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of graph theory (05C90) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Pattern recognition, speech recognition (68T10) Graph algorithms (graph-theoretic aspects) (05C85) Internet topics (68M11) Graphical methods in statistics (62A09)

Cites Work

• Maximum-likelihood estimation of the parameters of a multivariate normal distribution
• Gaussian Markov distributions over finite graphs
• Schur products and matrix completions
• Estimation of a covariance matrix with zeros
• A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
• The Structure and Function of Complex Networks
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models

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