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

Author(s)
Salter-Townshend, Michael  
White, Arthur  
Gollini, Isabella  
Murphy, Thomas Brendan  
Uri
http://hdl.handle.net/10197/3753
Date Issued
2012-08
Date Available
2012-08-17T16:25:41Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Wiley-Blackwell
Journal
Statistical Analysis and Data Mining
Volume
5
Issue
4
Start Page
243
End Page
264
Copyright (Published Version)
2012 Wiley Periodicals, Inc
Subjects

Social network analys...

Block models

ERGM

Latent space model

Mixed membership stoc...

Erdős-Renyi

Subject – LCSH
Social networks--Statistical methods
Random graphs
Latent structure analysis
Cluster analysis
DOI
10.1002/sam.11146
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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SNA_review.pdf

Size

692.07 KB

Format

Adobe PDF

Checksum (MD5)

0bc83cd1e73cff32ab8127abb9d7046c

Owning collection
Computer Science Research Collection
Mapped collections
CASL Research Collection•
Clique Research Collection•
Mathematics and Statistics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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