SEMINAR: Statistics Seminar

Network models are widely used to represent relational information among interacting units and the implications of these relations. In studies of social networks recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent a specified relationship between the actors.

The modeling of social networks 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 a 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.

Exponential family random graph models 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.

In this talk we review progress that has been made on networks observed in cross-sectional or longitudinally. We consider issues of the sampling of networks and partially- observed networks. We also consider latent cluster random effects models. We illustrate these methods using the "statnet" open-source software suite (http://statnet.org).

Biographical: Mark's research is based largely on motivation from questions in the social sciences. Recent focus has been on the development of statistical models for the analysis of social network data, survey sampling methods, inference for stochastic processes and demography. He received his B.Sc. from the University of Western Australia and his Ph.D. from the University of Chicago. Descriptions of his work are available at http://www.stat.ucla.edu/~handcock. Mark is visiting the School of Mathematics and Statistics (UWA) in 2010.