Mark Handcock: University of California, Los Angeles

In this talk we give an overview of social network analysis from the perspective of a statistician. The networks field is, and has been, broadly multidisciplinary with significant contributions from the social, natural and mathematical sciences. This has lead to a plethora of terminology, and network conceptualizations commensurate with the varied objectives of network analysis. As the primary focus of the social sciences has been the representation of social relations with the objective of understanding social structure, social scientists have been central to this development. We illustrate these ideas with Exponential-family random graph models (ERGM) which attempt to represent the complex dependencies in networks in a parsimonious, tractable and interpretable way. A major barrier to the application of such models has been lack of understanding of model behavior and a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the network structure produced by the models matches that of the data.

We will also consider latent cluster random effects models and touch upon issues of the sampling of networks and partially-observed networks.

We illustrate these methods using the “statnet” open-source software suite (http://statnet.org).