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Review of statistical network analysis: models, algorithms, and software

Authors

  • M. Salter-Townshend,

    Corresponding author
    1. Clique Strategic Research, Cluster, School of Mathematical Sciences & Complex and Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland
    • Clique Strategic Research, Cluster, School of Mathematical Sciences & Complex and Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland
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  • A. White,

    1. Clique Strategic Research, Cluster, School of Mathematical Sciences & Complex and Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland
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  • I. Gollini,

    1. Clique Strategic Research, Cluster, School of Mathematical Sciences & Complex and Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland
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  • T. B. Murphy

    1. Clique Strategic Research, Cluster, School of Mathematical Sciences & Complex and Adaptive Systems Laboratory, University College Dublin, Dublin 4, Ireland
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  • This material is based upon work supported by the Science Foundation Ireland under Grant No. 08/SRC/I1407: Clique: Graph & Network Analysis Cluster.

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 Erdős–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. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

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