Advantages of TERGM compared to Siena
In social psychology, group dynamics is one of the most important topics.To understand group dynamics, it is necessary to research the change of group network structure.There are two methods to analyze the group network data that evolve over time: TERGM(Hanneke et al. 2010; Krivitsky and Handcock 2014) and Siena(Snijders et al. 2010).TERGM(Temporal Exponential Random Graph Models) is an extension of ERGM to accommodate intertemporal dependence in longitudinally observed networks.It can use ERGM network terms and statistics to be reused in a dynamic context, understood in terms of formation and dissolution of edges.From network data at two or more points in time, Siena estimates that the network structure at the previous point in time affects the change in the relationship between actors at the later point in time by using agent-based simulations.In this study, we use TERGM and Siena to analyze similar network data and compare the results of each.Also, the advantages and disadvantages of each model will be identified.
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