Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles

Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are...

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Bibliographic Details
Published inSociological methods & research Vol. 48; no. 1; pp. 202 - 239
Main Authors Block, Per, Stadtfeld, Christoph, Snijders, Tom A. B.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.02.2019
SAGE PUBLICATIONS, INC
Subjects
Comparative Analysis
Differences
Network Analysis
Probability
Quantitative analysis
Social network analysis
Social Networks
Statistical Analysis
Siena
statistical network modeling
SAOM
ERGM
social networks
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Summary:Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.
ISSN:0049-1241
1552-8294
DOI:10.1177/0049124116672680