Alternative statistical models should reflect the uncertainty associated with a given observation, permit inference about the relative frequency about network substructures of theoretical interest, disambiguating the influence of confounding processes, efficiently representing complex structures, and linking local-level processes to global-level properties. However because network data is inherently relational, it violates the assumptions of independence and identical distribution of standard statistical models like linear regression. To support statistical inference on the processes influencing the formation of network structure, a statistical model should consider the set of all possible alternative networks weighted on their similarity to an observed network. This set of alternative networks may have similar or dissimilar structural features. However, these metrics describe the observed network which is only one instance of a large number of possible alternative networks. Many metrics exist to describe the structural features of an observed network such as the density, centrality, or assortativity. Examples of networks examined using ERGM include knowledge networks, organizational networks, colleague networks, social media networks, networks of scientific development, and others. Exponential Random Graph Models (ERGMs) are a family of statistical models for analyzing data from social and other networks.
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