Social Network Models: Statistical

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Abstract

Recent developments in statistical models for social networks reflect an increasing theoretical focus in the social and behavioral sciences on the interdependence of social actors in dynamic, network-based social settings. As a result, a growing importance has been accorded the problem of modeling the dynamic and complex interdependencies among network ties and the actions of the individuals whom they link. The early focus of statistical network modeling on the mathematical and statistical properties of Bernoulli and dyad-independent random graph distributions is reviewed. These early efforts have now been replaced by distributions that consist of theoretically and empirically plausible parametric models for structural network phenomena. These new models, discussed in detail here, allow complicated dependencies among the network ties, and have many possible special cases, reflecting the complexity of the social network paradigm.

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