Encyclopedia of Social Network Analysis and Mining

2014 Edition
| Editors: Reda Alhajj, Jon Rokne

Siena: Statistical Modeling of Longitudinal Network Data

  • Tom A. B. Snijders
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6170-8_312



Network Panel Data

Longitudinal data consisting of two or more repeated observations of a network on a given set of nodes

Panel Wave

The data observed for one given observation moment in a panel study

Social Actors

Individuals, companies, etc., represented by the nodes in the network

Stochastic Actor-Oriented Model

A probability model for network dynamics where changes may take place at arbitrary moments in continuous time and where these changes are regarded as consequences of choices made by the actors


R package implementing statistical inference according to a stochastic actor-oriented model given network panel data


Model components defining the probabilities of tie changes in the stochastic actor-oriented model

Method of Moments

One of the traditional methods in statistics for parameter estimation

Dependent Variable

The variable...

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Recommended Reading

  1. In addition to the help pages that are available as for all R packages, there is an extensive manual (Ripley et al. 2013) and a tutorial paper (Snij-ders et al. 2010b). A textbook about the Siena method and an edited volume with example applications are in preparation. The website http://www.stats.ox.ac.uk/siena/ is actively maintained and contains references to the basic methodology, references to applications, R scripts, example data sets, workshop announcements, and more.
  2. For those who wish to read more about the mathematical and methodological background, a recommended sequence of readings could be Snijders (1996) as an introduction to the idea of stochastic actor-oriented models, Snijders (2001) or Snijders (2005) for the basic definition of the model for one dependent network defined as a changing digraph, and Steglich et al. (2010) for models for the dynamics of networks and behavior, which might be followed by Snijders et al. (2010a) for Maximum Likelihood estimation or Snijders et al. (2013) for models with multiple dependent networks.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Tom A. B. Snijders
    • 1
    • 2
  1. 1.Department of Statistics and Nuffield CollegeUniversity of OxfordOxfordUK
  2. 2.Department of SociologyUniversity of GroningenGroningenThe Netherlands