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Given the recurring discussions on this topic in the SOCNET community, we would like to draw your attention to a paper we recently published in Sociological Methods and Research that outlines and illustrates the principal differences between ERGMs and SAOMs (Siena models). The comparison uses cross-sectional variants to understand the fundamental differences between tie-based and actor-based modelling of networks; however, the discussed differences also apply to longitudinal versions of both models. The main results of this paper were already presented at the Sunbelt 2015 in Brighton. The link to the paper and the abstract are provided below.
Per Block, Christoph Stadtfeld and Tom Snijders
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 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.