Review of Statistical Network Analysis: Models, Algorithms, and Software

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Title: Review of Statistical Network Analysis: Models, Algorithms, and Software
Authors: Salter-Townshend, Michael
White, Arthur
Gollini, Isabella
Murphy, Thomas Brendan
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Date: Aug-2012
Abstract: The analysis of network data is an area that is rapidly growing, both within and outside of the discipline of statistics. This review provides a concise summary of methods and models used in the statistical analysis of network data, including the Erdos–Renyi model, the exponential family class of network models, and recently developed latent variable models. Many of the methods and models are illustrated by application to the well-known Zachary karate dataset. Software routines available for implementing methods are emphasized throughout. The aim of this paper is to provide a review with enough detail about many common classes of network models to whet the appetite and to point the way to further reading.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Wiley-Blackwell
Copyright (published version): 2012 Wiley Periodicals, Inc
Keywords: Social network analysisBlock modelsERGMLatent space modelMixed membership stochastic blockmodelErdős-Renyi
Subject LCSH: Social networks--Statistical methods
Random graphs
Latent structure analysis
Cluster analysis
DOI: 10.1002/sam.11146
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
Mathematics and Statistics Research Collection
Clique Research Collection
CASL Research Collection

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